Overview

Brought to you by YData

Dataset statistics

Number of variables106
Number of observations177837
Missing cells0
Missing cells (%)0.0%
Duplicate rows4930
Duplicate rows (%)2.8%
Total size in memory143.8 MiB
Average record size in memory848.0 B

Variable types

Numeric6
Categorical100

Alerts

Dataset has 4930 (2.8%) duplicate rowsDuplicates
Amount(in rupees) is highly overall correlated with Area and 2 other fieldsHigh correlation
Area is highly overall correlated with Amount(in rupees) and 2 other fieldsHigh correlation
BHK is highly overall correlated with Amount(in rupees) and 2 other fieldsHigh correlation
Bathroom is highly overall correlated with Amount(in rupees) and 2 other fieldsHigh correlation
Furnishing_Semi-Furnished is highly overall correlated with Furnishing_UnfurnishedHigh correlation
Furnishing_Unfurnished is highly overall correlated with Furnishing_Semi-FurnishedHigh correlation
Ownership_Co-operative Society is highly overall correlated with Ownership_FreeholdHigh correlation
Ownership_Freehold is highly overall correlated with Ownership_Co-operative Society and 1 other fieldsHigh correlation
Ownership_Leasehold is highly overall correlated with Ownership_Freehold and 1 other fieldsHigh correlation
Transaction_New Property is highly overall correlated with Transaction_ResaleHigh correlation
Transaction_Resale is highly overall correlated with Transaction_New PropertyHigh correlation
facing_East is highly overall correlated with facing_North - EastHigh correlation
facing_North - East is highly overall correlated with facing_EastHigh correlation
location_greater-noida is highly overall correlated with Ownership_LeaseholdHigh correlation
location_agra is highly imbalanced (97.5%) Imbalance
location_ahmadnagar is highly imbalanced (99.8%) Imbalance
location_ahmedabad is highly imbalanced (63.1%) Imbalance
location_allahabad is highly imbalanced (98.9%) Imbalance
location_aurangabad is highly imbalanced (97.5%) Imbalance
location_badlapur is highly imbalanced (97.8%) Imbalance
location_belgaum is highly imbalanced (99.6%) Imbalance
location_bhiwadi is highly imbalanced (94.8%) Imbalance
location_bhiwandi is highly imbalanced (99.4%) Imbalance
location_bhopal is highly imbalanced (99.2%) Imbalance
location_bhubaneswar is highly imbalanced (97.7%) Imbalance
location_chandigarh is highly imbalanced (93.4%) Imbalance
location_chennai is highly imbalanced (68.4%) Imbalance
location_coimbatore is highly imbalanced (96.8%) Imbalance
location_dehradun is highly imbalanced (95.2%) Imbalance
location_durgapur is highly imbalanced (99.0%) Imbalance
location_ernakulam is highly imbalanced (98.9%) Imbalance
location_faridabad is highly imbalanced (85.3%) Imbalance
location_ghaziabad is highly imbalanced (95.4%) Imbalance
location_goa is highly imbalanced (94.4%) Imbalance
location_greater-noida is highly imbalanced (83.0%) Imbalance
location_guntur is highly imbalanced (98.4%) Imbalance
location_gurgaon is highly imbalanced (51.2%) Imbalance
location_guwahati is highly imbalanced (96.1%) Imbalance
location_gwalior is highly imbalanced (99.0%) Imbalance
location_haridwar is highly imbalanced (98.9%) Imbalance
location_hyderabad is highly imbalanced (66.2%) Imbalance
location_indore is highly imbalanced (99.2%) Imbalance
location_jabalpur is highly imbalanced (99.2%) Imbalance
location_jaipur is highly imbalanced (73.9%) Imbalance
location_jamshedpur is highly imbalanced (96.6%) Imbalance
location_jodhpur is highly imbalanced (99.6%) Imbalance
location_kalyan is highly imbalanced (96.8%) Imbalance
location_kanpur is highly imbalanced (96.0%) Imbalance
location_kochi is highly imbalanced (94.9%) Imbalance
location_kozhikode is highly imbalanced (99.6%) Imbalance
location_lucknow is highly imbalanced (95.8%) Imbalance
location_ludhiana is highly imbalanced (99.0%) Imbalance
location_madurai is highly imbalanced (99.8%) Imbalance
location_mangalore is highly imbalanced (97.5%) Imbalance
location_mohali is highly imbalanced (93.1%) Imbalance
location_mumbai is highly imbalanced (91.8%) Imbalance
location_mysore is highly imbalanced (98.8%) Imbalance
location_nagpur is highly imbalanced (96.6%) Imbalance
location_nashik is highly imbalanced (98.5%) Imbalance
location_navi-mumbai is highly imbalanced (96.3%) Imbalance
location_navsari is highly imbalanced (99.8%) Imbalance
location_nellore is highly imbalanced (99.8%) Imbalance
location_noida is highly imbalanced (94.0%) Imbalance
location_palakkad is highly imbalanced (99.8%) Imbalance
location_palghar is highly imbalanced (97.2%) Imbalance
location_panchkula is highly imbalanced (98.0%) Imbalance
location_patna is highly imbalanced (96.0%) Imbalance
location_pondicherry is highly imbalanced (99.8%) Imbalance
location_pune is highly imbalanced (90.5%) Imbalance
location_raipur is highly imbalanced (97.2%) Imbalance
location_rajahmundry is highly imbalanced (99.4%) Imbalance
location_ranchi is highly imbalanced (94.7%) Imbalance
location_satara is highly imbalanced (99.6%) Imbalance
location_shimla is highly imbalanced (99.6%) Imbalance
location_siliguri is highly imbalanced (97.8%) Imbalance
location_solapur is highly imbalanced (99.8%) Imbalance
location_sonipat is highly imbalanced (96.1%) Imbalance
location_surat is highly imbalanced (90.5%) Imbalance
location_thane is highly imbalanced (91.6%) Imbalance
location_thrissur is highly imbalanced (98.7%) Imbalance
location_tirupati is highly imbalanced (99.4%) Imbalance
location_trichy is highly imbalanced (99.2%) Imbalance
location_trivandrum is highly imbalanced (98.7%) Imbalance
location_udaipur is highly imbalanced (99.2%) Imbalance
location_udupi is highly imbalanced (99.6%) Imbalance
location_vadodara is highly imbalanced (89.8%) Imbalance
location_vapi is highly imbalanced (99.0%) Imbalance
location_varanasi is highly imbalanced (98.4%) Imbalance
location_vijayawada is highly imbalanced (97.0%) Imbalance
location_visakhapatnam is highly imbalanced (92.1%) Imbalance
location_vrindavan is highly imbalanced (99.6%) Imbalance
location_zirakpur is highly imbalanced (93.1%) Imbalance
Transaction_Other is highly imbalanced (96.3%) Imbalance
Transaction_Rent/Lease is highly imbalanced (> 99.9%) Imbalance
Furnishing_Furnished is highly imbalanced (50.2%) Imbalance
facing_North is highly imbalanced (57.7%) Imbalance
facing_North - West is highly imbalanced (85.1%) Imbalance
facing_South is highly imbalanced (83.5%) Imbalance
facing_South - East is highly imbalanced (89.1%) Imbalance
facing_South -West is highly imbalanced (90.9%) Imbalance
facing_West is highly imbalanced (72.4%) Imbalance
Ownership_Co-operative Society is highly imbalanced (86.5%) Imbalance
Ownership_Freehold is highly imbalanced (69.9%) Imbalance
Ownership_Leasehold is highly imbalanced (81.1%) Imbalance
Ownership_Power Of Attorney is highly imbalanced (94.9%) Imbalance
BHK is highly skewed (γ1 = 117.4895518) Skewed
Area is highly skewed (γ1 = 211.238879) Skewed
Floor has 10584 (6.0%) zeros Zeros

Reproduction

Analysis started2025-09-15 04:59:35.294669
Analysis finished2025-09-15 05:01:13.500314
Duration1 minute and 38.21 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Amount(in rupees)
Real number (ℝ)

High correlation 

Distinct1550
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.922852
Minimum11.512935
Maximum19.989297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-09-15T10:31:13.600890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11.512935
5-th percentile14.626441
Q115.390357
median15.869634
Q316.489659
95-th percentile17.330037
Maximum19.989297
Range8.4763612
Interquartile range (IQR)1.099302

Descriptive statistics

Standard deviation0.8209575
Coefficient of variation (CV)0.051558446
Kurtosis0.048816532
Mean15.922852
Median Absolute Deviation (MAD)0.5485659
Skewness0.27497286
Sum2831672.2
Variance0.67397122
MonotonicityNot monotonic
2025-09-15T10:31:13.757049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.95557684 5264
 
3.0%
15.68731289 4229
 
2.4%
15.60727019 3869
 
2.2%
15.76142085 3801
 
2.1%
15.06827381 3369
 
1.9%
15.83041371 3144
 
1.8%
16.01273525 3143
 
1.8%
15.20180517 3098
 
1.7%
15.42494867 3006
 
1.7%
16.6777115 2879
 
1.6%
Other values (1540) 142035
79.9%
ValueCountFrequency (%)
11.51293546 5
< 0.1%
11.60824474 1
 
< 0.1%
11.69525536 1
 
< 0.1%
11.84940484 2
 
< 0.1%
11.91839724 1
 
< 0.1%
12.10071769 1
 
< 0.1%
12.20607765 4
< 0.1%
12.25486757 1
 
< 0.1%
12.30138737 2
 
< 0.1%
12.38839837 1
 
< 0.1%
ValueCountFrequency (%)
19.98929666 1
 
< 0.1%
19.85576527 3
< 0.1%
19.80697511 3
< 0.1%
19.7235935 2
 
< 0.1%
19.70161459 6
< 0.1%
19.64445618 1
 
< 0.1%
19.58383156 1
 
< 0.1%
19.51929304 4
< 0.1%
19.45030016 1
 
< 0.1%
19.41393252 6
< 0.1%

Floor
Real number (ℝ)

Zeros 

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3993376
Minimum-2
Maximum75
Zeros10584
Zeros (%)6.0%
Negative334
Negative (%)0.2%
Memory size1.4 MiB
2025-09-15T10:31:13.901870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q12
median3
Q35
95-th percentile14
Maximum75
Range77
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.6109835
Coefficient of variation (CV)1.0481086
Kurtosis10.112522
Mean4.3993376
Median Absolute Deviation (MAD)2
Skewness2.5169035
Sum782365
Variance21.261169
MonotonicityNot monotonic
2025-09-15T10:31:14.043319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 38008
21.4%
1 30945
17.4%
3 24924
14.0%
4 17225
9.7%
5 11848
 
6.7%
0 10584
 
6.0%
6 6864
 
3.9%
7 6648
 
3.7%
8 5193
 
2.9%
10 4977
 
2.8%
Other values (44) 20621
11.6%
ValueCountFrequency (%)
-2 124
 
0.1%
-1 210
 
0.1%
0 10584
 
6.0%
1 30945
17.4%
2 38008
21.4%
3 24924
14.0%
4 17225
9.7%
5 11848
 
6.7%
6 6864
 
3.9%
7 6648
 
3.7%
ValueCountFrequency (%)
75 2
< 0.1%
70 2
< 0.1%
63 2
< 0.1%
60 4
< 0.1%
59 2
< 0.1%
51 2
< 0.1%
50 1
 
< 0.1%
46 2
< 0.1%
45 3
< 0.1%
44 2
< 0.1%

Bathroom
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4458746
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-09-15T10:31:14.168447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84729577
Coefficient of variation (CV)0.34641832
Kurtosis0.79229847
Mean2.4458746
Median Absolute Deviation (MAD)0
Skewness0.71511609
Sum434967
Variance0.71791013
MonotonicityNot monotonic
2025-09-15T10:31:14.263024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 89756
50.5%
3 54186
30.5%
1 15509
 
8.7%
4 14825
 
8.3%
5 3278
 
1.8%
6 283
 
0.2%
ValueCountFrequency (%)
1 15509
 
8.7%
2 89756
50.5%
3 54186
30.5%
4 14825
 
8.3%
5 3278
 
1.8%
6 283
 
0.2%
ValueCountFrequency (%)
6 283
 
0.2%
5 3278
 
1.8%
4 14825
 
8.3%
3 54186
30.5%
2 89756
50.5%
1 15509
 
8.7%

Balcony
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0086146
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-09-15T10:31:14.356329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81259431
Coefficient of variation (CV)0.40455461
Kurtosis1.233052
Mean2.0086146
Median Absolute Deviation (MAD)0
Skewness0.8869604
Sum357206
Variance0.66030951
MonotonicityNot monotonic
2025-09-15T10:31:14.466870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 97468
54.8%
1 45143
25.4%
3 24956
 
14.0%
4 9274
 
5.2%
5 813
 
0.5%
6 183
 
0.1%
ValueCountFrequency (%)
1 45143
25.4%
2 97468
54.8%
3 24956
 
14.0%
4 9274
 
5.2%
5 813
 
0.5%
6 183
 
0.1%
ValueCountFrequency (%)
6 183
 
0.1%
5 813
 
0.5%
4 9274
 
5.2%
3 24956
 
14.0%
2 97468
54.8%
1 45143
25.4%

BHK
Real number (ℝ)

High correlation  Skewed 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.601354
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-09-15T10:31:14.575277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum360
Range359
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3624722
Coefficient of variation (CV)0.52375501
Kurtosis27567.549
Mean2.601354
Median Absolute Deviation (MAD)1
Skewness117.48955
Sum462617
Variance1.8563305
MonotonicityNot monotonic
2025-09-15T10:31:14.684329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3 78940
44.4%
2 70483
39.6%
4 16336
 
9.2%
1 10740
 
6.0%
5 1139
 
0.6%
6 92
 
0.1%
10 39
 
< 0.1%
7 21
 
< 0.1%
8 16
 
< 0.1%
9 7
 
< 0.1%
Other values (15) 24
 
< 0.1%
ValueCountFrequency (%)
1 10740
 
6.0%
2 70483
39.6%
3 78940
44.4%
4 16336
 
9.2%
5 1139
 
0.6%
6 92
 
0.1%
7 21
 
< 0.1%
8 16
 
< 0.1%
9 7
 
< 0.1%
10 39
 
< 0.1%
ValueCountFrequency (%)
360 1
 
< 0.1%
109 1
 
< 0.1%
94 2
< 0.1%
86 2
< 0.1%
75 2
< 0.1%
74 3
< 0.1%
68 1
 
< 0.1%
67 2
< 0.1%
62 1
 
< 0.1%
49 2
< 0.1%

Area
Real number (ℝ)

High correlation  Skewed 

Distinct2534
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1201.9775
Minimum1
Maximum709222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-09-15T10:31:14.980213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile422
Q1825
median1086
Q31323
95-th percentile2100
Maximum709222
Range709221
Interquartile range (IQR)498

Descriptive statistics

Standard deviation2388.808
Coefficient of variation (CV)1.9873983
Kurtosis55154.35
Mean1201.9775
Median Absolute Deviation (MAD)261
Skewness211.23888
Sum2.1375607 × 108
Variance5706403.5
MonotonicityNot monotonic
2025-09-15T10:31:15.137356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1323 34018
19.1%
825 33009
18.6%
422 5791
 
3.3%
1000 5223
 
2.9%
900 4660
 
2.6%
2100 4586
 
2.6%
1300 3446
 
1.9%
1600 2737
 
1.5%
600 2210
 
1.2%
1500 2130
 
1.2%
Other values (2524) 80027
45.0%
ValueCountFrequency (%)
1 3
< 0.1%
2 1
 
< 0.1%
5 1
 
< 0.1%
12 1
 
< 0.1%
17 1
 
< 0.1%
20 2
< 0.1%
21 1
 
< 0.1%
25 4
< 0.1%
27 1
 
< 0.1%
30 1
 
< 0.1%
ValueCountFrequency (%)
709222 1
< 0.1%
495970 1
< 0.1%
282004 1
< 0.1%
194936 1
< 0.1%
113134 1
< 0.1%
107806 1
< 0.1%
81845 1
< 0.1%
81675 1
< 0.1%
72009 1
< 0.1%
71775 1
< 0.1%

location_agra
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177394 
1
 
443

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177394
99.8%
1 443
 
0.2%

Length

2025-09-15T10:31:15.263091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:15.356379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177394
99.8%
1 443
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 177394
99.8%
1 443
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177394
99.8%
1 443
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177394
99.8%
1 443
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177394
99.8%
1 443
 
0.2%

location_ahmadnagar
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177807 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Length

2025-09-15T10:31:15.474169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:15.575265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

location_ahmedabad
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
165223 
1
 
12614

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 165223
92.9%
1 12614
 
7.1%

Length

2025-09-15T10:31:15.670111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:15.778008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 165223
92.9%
1 12614
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 165223
92.9%
1 12614
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 165223
92.9%
1 12614
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 165223
92.9%
1 12614
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 165223
92.9%
1 12614
 
7.1%

location_allahabad
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177661 
1
 
176

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177661
99.9%
1 176
 
0.1%

Length

2025-09-15T10:31:15.887129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:15.989161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177661
99.9%
1 176
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177661
99.9%
1 176
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177661
99.9%
1 176
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177661
99.9%
1 176
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177661
99.9%
1 176
 
0.1%

location_aurangabad
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177393 
1
 
444

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177393
99.8%
1 444
 
0.2%

Length

2025-09-15T10:31:16.105117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:16.199609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177393
99.8%
1 444
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 177393
99.8%
1 444
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177393
99.8%
1 444
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177393
99.8%
1 444
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177393
99.8%
1 444
 
0.2%

location_badlapur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177449 
1
 
388

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%

Length

2025-09-15T10:31:16.309431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:16.402439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
154575 
1
23262 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154575
86.9%
1 23262
 
13.1%

Length

2025-09-15T10:31:16.511388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:16.622081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 154575
86.9%
1 23262
 
13.1%

Most occurring characters

ValueCountFrequency (%)
0 154575
86.9%
1 23262
 
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154575
86.9%
1 23262
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154575
86.9%
1 23262
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154575
86.9%
1 23262
 
13.1%

location_belgaum
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177777 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Length

2025-09-15T10:31:16.729815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:16.831223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

location_bhiwadi
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176802 
1
 
1035

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176802
99.4%
1 1035
 
0.6%

Length

2025-09-15T10:31:16.936150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:17.028440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176802
99.4%
1 1035
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176802
99.4%
1 1035
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176802
99.4%
1 1035
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176802
99.4%
1 1035
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176802
99.4%
1 1035
 
0.6%

location_bhiwandi
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177749 
1
 
88

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177749
> 99.9%
1 88
 
< 0.1%

Length

2025-09-15T10:31:17.145513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:17.238526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177749
> 99.9%
1 88
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177749
> 99.9%
1 88
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177749
> 99.9%
1 88
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177749
> 99.9%
1 88
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177749
> 99.9%
1 88
 
< 0.1%

location_bhopal
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177719 
1
 
118

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177719
99.9%
1 118
 
0.1%

Length

2025-09-15T10:31:17.341246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:17.451269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177719
99.9%
1 118
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177719
99.9%
1 118
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177719
99.9%
1 118
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177719
99.9%
1 118
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177719
99.9%
1 118
 
0.1%

location_bhubaneswar
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177445 
1
 
392

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177445
99.8%
1 392
 
0.2%

Length

2025-09-15T10:31:17.559760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:17.662057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177445
99.8%
1 392
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 177445
99.8%
1 392
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177445
99.8%
1 392
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177445
99.8%
1 392
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177445
99.8%
1 392
 
0.2%

location_chandigarh
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176435 
1
 
1402

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176435
99.2%
1 1402
 
0.8%

Length

2025-09-15T10:31:17.762789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:17.858028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176435
99.2%
1 1402
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 176435
99.2%
1 1402
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176435
99.2%
1 1402
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176435
99.2%
1 1402
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176435
99.2%
1 1402
 
0.8%

location_chennai
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
167674 
1
 
10163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 167674
94.3%
1 10163
 
5.7%

Length

2025-09-15T10:31:17.973258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:18.075581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 167674
94.3%
1 10163
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 167674
94.3%
1 10163
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 167674
94.3%
1 10163
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 167674
94.3%
1 10163
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 167674
94.3%
1 10163
 
5.7%

location_coimbatore
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177251 
1
 
586

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177251
99.7%
1 586
 
0.3%

Length

2025-09-15T10:31:18.184387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:18.277876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177251
99.7%
1 586
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 177251
99.7%
1 586
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177251
99.7%
1 586
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177251
99.7%
1 586
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177251
99.7%
1 586
 
0.3%

location_dehradun
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176889 
1
 
948

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176889
99.5%
1 948
 
0.5%

Length

2025-09-15T10:31:18.387535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:18.496180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176889
99.5%
1 948
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 176889
99.5%
1 948
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176889
99.5%
1 948
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176889
99.5%
1 948
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176889
99.5%
1 948
 
0.5%

location_durgapur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177689 
1
 
148

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

Length

2025-09-15T10:31:18.590579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:18.698947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

location_ernakulam
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177658 
1
 
179

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177658
99.9%
1 179
 
0.1%

Length

2025-09-15T10:31:18.808622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:18.914983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177658
99.9%
1 179
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177658
99.9%
1 179
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177658
99.9%
1 179
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177658
99.9%
1 179
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177658
99.9%
1 179
 
0.1%

location_faridabad
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
174104 
1
 
3733

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174104
97.9%
1 3733
 
2.1%

Length

2025-09-15T10:31:19.024099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:19.126973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 174104
97.9%
1 3733
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 174104
97.9%
1 3733
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 174104
97.9%
1 3733
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 174104
97.9%
1 3733
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 174104
97.9%
1 3733
 
2.1%

location_ghaziabad
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176942 
1
 
895

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176942
99.5%
1 895
 
0.5%

Length

2025-09-15T10:31:19.235535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:19.325845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176942
99.5%
1 895
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 176942
99.5%
1 895
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176942
99.5%
1 895
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176942
99.5%
1 895
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176942
99.5%
1 895
 
0.5%

location_goa
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176684 
1
 
1153

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176684
99.4%
1 1153
 
0.6%

Length

2025-09-15T10:31:19.446699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:19.544980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176684
99.4%
1 1153
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176684
99.4%
1 1153
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176684
99.4%
1 1153
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176684
99.4%
1 1153
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176684
99.4%
1 1153
 
0.6%

location_greater-noida
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
173347 
1
 
4490

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173347
97.5%
1 4490
 
2.5%

Length

2025-09-15T10:31:19.654360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:19.747353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 173347
97.5%
1 4490
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 173347
97.5%
1 4490
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 173347
97.5%
1 4490
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 173347
97.5%
1 4490
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 173347
97.5%
1 4490
 
2.5%

location_guntur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177569 
1
 
268

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177569
99.8%
1 268
 
0.2%

Length

2025-09-15T10:31:20.011587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:20.121608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177569
99.8%
1 268
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 177569
99.8%
1 268
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177569
99.8%
1 268
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177569
99.8%
1 268
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177569
99.8%
1 268
 
0.2%

location_gurgaon
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
158996 
1
18841 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 158996
89.4%
1 18841
 
10.6%

Length

2025-09-15T10:31:20.230207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:20.333548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 158996
89.4%
1 18841
 
10.6%

Most occurring characters

ValueCountFrequency (%)
0 158996
89.4%
1 18841
 
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 158996
89.4%
1 18841
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 158996
89.4%
1 18841
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 158996
89.4%
1 18841
 
10.6%

location_guwahati
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177087 
1
 
750

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177087
99.6%
1 750
 
0.4%

Length

2025-09-15T10:31:20.442519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:20.531003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177087
99.6%
1 750
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 177087
99.6%
1 750
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177087
99.6%
1 750
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177087
99.6%
1 750
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177087
99.6%
1 750
 
0.4%

location_gwalior
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177689 
1
 
148

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

Length

2025-09-15T10:31:20.639648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:20.746398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177689
99.9%
1 148
 
0.1%

location_haridwar
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177660 
1
 
177

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177660
99.9%
1 177
 
0.1%

Length

2025-09-15T10:31:20.858936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:20.950828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177660
99.9%
1 177
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177660
99.9%
1 177
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177660
99.9%
1 177
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177660
99.9%
1 177
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177660
99.9%
1 177
 
0.1%

location_hyderabad
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
166690 
1
 
11147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 166690
93.7%
1 11147
 
6.3%

Length

2025-09-15T10:31:21.060280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:21.164635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 166690
93.7%
1 11147
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 166690
93.7%
1 11147
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 166690
93.7%
1 11147
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 166690
93.7%
1 11147
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 166690
93.7%
1 11147
 
6.3%

location_indore
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177717 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177717
99.9%
1 120
 
0.1%

Length

2025-09-15T10:31:21.262757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:21.373506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177717
99.9%
1 120
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177717
99.9%
1 120
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177717
99.9%
1 120
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177717
99.9%
1 120
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177717
99.9%
1 120
 
0.1%

location_jabalpur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177718 
1
 
119

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Length

2025-09-15T10:31:21.464756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:21.575139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

location_jaipur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
169970 
1
 
7867

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 169970
95.6%
1 7867
 
4.4%

Length

2025-09-15T10:31:21.690665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:21.779223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 169970
95.6%
1 7867
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 169970
95.6%
1 7867
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 169970
95.6%
1 7867
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 169970
95.6%
1 7867
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 169970
95.6%
1 7867
 
4.4%

location_jamshedpur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177215 
1
 
622

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177215
99.7%
1 622
 
0.3%

Length

2025-09-15T10:31:21.887056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:21.996373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177215
99.7%
1 622
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 177215
99.7%
1 622
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177215
99.7%
1 622
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177215
99.7%
1 622
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177215
99.7%
1 622
 
0.3%

location_jodhpur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177777 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Length

2025-09-15T10:31:22.106104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:22.207924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

location_kalyan
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177243 
1
 
594

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177243
99.7%
1 594
 
0.3%

Length

2025-09-15T10:31:22.309122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:22.414261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177243
99.7%
1 594
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 177243
99.7%
1 594
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177243
99.7%
1 594
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177243
99.7%
1 594
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177243
99.7%
1 594
 
0.3%

location_kanpur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177076 
1
 
761

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177076
99.6%
1 761
 
0.4%

Length

2025-09-15T10:31:22.522978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:22.624585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177076
99.6%
1 761
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 177076
99.6%
1 761
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177076
99.6%
1 761
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177076
99.6%
1 761
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177076
99.6%
1 761
 
0.4%

location_kochi
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176808 
1
 
1029

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176808
99.4%
1 1029
 
0.6%

Length

2025-09-15T10:31:22.733397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:22.825699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176808
99.4%
1 1029
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176808
99.4%
1 1029
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176808
99.4%
1 1029
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176808
99.4%
1 1029
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176808
99.4%
1 1029
 
0.6%

location_kolkata
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
156232 
1
21605 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 156232
87.9%
1 21605
 
12.1%

Length

2025-09-15T10:31:22.935842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:23.048137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 156232
87.9%
1 21605
 
12.1%

Most occurring characters

ValueCountFrequency (%)
0 156232
87.9%
1 21605
 
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 156232
87.9%
1 21605
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 156232
87.9%
1 21605
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 156232
87.9%
1 21605
 
12.1%

location_kozhikode
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177778 
1
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Length

2025-09-15T10:31:23.154368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:23.261682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

location_lucknow
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177021 
1
 
816

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177021
99.5%
1 816
 
0.5%

Length

2025-09-15T10:31:23.372217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:23.468016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177021
99.5%
1 816
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 177021
99.5%
1 816
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177021
99.5%
1 816
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177021
99.5%
1 816
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177021
99.5%
1 816
 
0.5%

location_ludhiana
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177691 
1
 
146

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177691
99.9%
1 146
 
0.1%

Length

2025-09-15T10:31:23.575469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:23.684850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177691
99.9%
1 146
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177691
99.9%
1 146
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177691
99.9%
1 146
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177691
99.9%
1 146
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177691
99.9%
1 146
 
0.1%

location_madurai
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177811 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177811
> 99.9%
1 26
 
< 0.1%

Length

2025-09-15T10:31:23.777754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:23.893235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177811
> 99.9%
1 26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177811
> 99.9%
1 26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177811
> 99.9%
1 26
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177811
> 99.9%
1 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177811
> 99.9%
1 26
 
< 0.1%

location_mangalore
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177395 
1
 
442

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177395
99.8%
1 442
 
0.2%

Length

2025-09-15T10:31:23.999198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:24.099930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177395
99.8%
1 442
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 177395
99.8%
1 442
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177395
99.8%
1 442
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177395
99.8%
1 442
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177395
99.8%
1 442
 
0.2%

location_mohali
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176358 
1
 
1479

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176358
99.2%
1 1479
 
0.8%

Length

2025-09-15T10:31:24.206790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:24.309085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176358
99.2%
1 1479
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 176358
99.2%
1 1479
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176358
99.2%
1 1479
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176358
99.2%
1 1479
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176358
99.2%
1 1479
 
0.8%

location_mumbai
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176023 
1
 
1814

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176023
99.0%
1 1814
 
1.0%

Length

2025-09-15T10:31:24.415781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:24.512802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176023
99.0%
1 1814
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 176023
99.0%
1 1814
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176023
99.0%
1 1814
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176023
99.0%
1 1814
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176023
99.0%
1 1814
 
1.0%

location_mysore
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177657 
1
 
180

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177657
99.9%
1 180
 
0.1%

Length

2025-09-15T10:31:24.606825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:24.725252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177657
99.9%
1 180
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177657
99.9%
1 180
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177657
99.9%
1 180
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177657
99.9%
1 180
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177657
99.9%
1 180
 
0.1%

location_nagpur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177211 
1
 
626

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177211
99.6%
1 626
 
0.4%

Length

2025-09-15T10:31:24.825062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:24.932947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177211
99.6%
1 626
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 177211
99.6%
1 626
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177211
99.6%
1 626
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177211
99.6%
1 626
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177211
99.6%
1 626
 
0.4%

location_nashik
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177593 
1
 
244

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177593
99.9%
1 244
 
0.1%

Length

2025-09-15T10:31:25.039115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:25.141082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177593
99.9%
1 244
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177593
99.9%
1 244
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177593
99.9%
1 244
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177593
99.9%
1 244
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177593
99.9%
1 244
 
0.1%

location_navi-mumbai
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177146 
1
 
691

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177146
99.6%
1 691
 
0.4%

Length

2025-09-15T10:31:25.388190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:25.481216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177146
99.6%
1 691
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 177146
99.6%
1 691
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177146
99.6%
1 691
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177146
99.6%
1 691
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177146
99.6%
1 691
 
0.4%

location_navsari
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177807 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Length

2025-09-15T10:31:25.590426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:25.703100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

location_nellore
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177808 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%

Length

2025-09-15T10:31:25.808291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:25.911528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
152892 
1
24945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 152892
86.0%
1 24945
 
14.0%

Length

2025-09-15T10:31:26.017370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:26.106803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 152892
86.0%
1 24945
 
14.0%

Most occurring characters

ValueCountFrequency (%)
0 152892
86.0%
1 24945
 
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 152892
86.0%
1 24945
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 152892
86.0%
1 24945
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 152892
86.0%
1 24945
 
14.0%

location_noida
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176590 
1
 
1247

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176590
99.3%
1 1247
 
0.7%

Length

2025-09-15T10:31:26.231230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:26.331994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176590
99.3%
1 1247
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 176590
99.3%
1 1247
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176590
99.3%
1 1247
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176590
99.3%
1 1247
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176590
99.3%
1 1247
 
0.7%

location_palakkad
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177807 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Length

2025-09-15T10:31:26.437579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:26.529167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

location_palghar
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177341 
1
 
496

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177341
99.7%
1 496
 
0.3%

Length

2025-09-15T10:31:26.638663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:26.746191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177341
99.7%
1 496
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 177341
99.7%
1 496
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177341
99.7%
1 496
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177341
99.7%
1 496
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177341
99.7%
1 496
 
0.3%

location_panchkula
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177507 
1
 
330

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177507
99.8%
1 330
 
0.2%

Length

2025-09-15T10:31:26.841460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:26.955560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177507
99.8%
1 330
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 177507
99.8%
1 330
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177507
99.8%
1 330
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177507
99.8%
1 330
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177507
99.8%
1 330
 
0.2%

location_patna
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177066 
1
 
771

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177066
99.6%
1 771
 
0.4%

Length

2025-09-15T10:31:27.059937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:27.153277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177066
99.6%
1 771
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 177066
99.6%
1 771
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177066
99.6%
1 771
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177066
99.6%
1 771
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177066
99.6%
1 771
 
0.4%

location_pondicherry
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177808 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%

Length

2025-09-15T10:31:27.271341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:27.367376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177808
> 99.9%
1 29
 
< 0.1%

location_pune
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
175660 
1
 
2177

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175660
98.8%
1 2177
 
1.2%

Length

2025-09-15T10:31:27.465000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:27.574818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 175660
98.8%
1 2177
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 175660
98.8%
1 2177
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 175660
98.8%
1 2177
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 175660
98.8%
1 2177
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 175660
98.8%
1 2177
 
1.2%

location_raipur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177343 
1
 
494

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177343
99.7%
1 494
 
0.3%

Length

2025-09-15T10:31:27.683927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:27.777677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177343
99.7%
1 494
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 177343
99.7%
1 494
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177343
99.7%
1 494
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177343
99.7%
1 494
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177343
99.7%
1 494
 
0.3%

location_rajahmundry
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177747 
1
 
90

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

Length

2025-09-15T10:31:27.888052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:27.997556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

location_ranchi
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176772 
1
 
1065

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176772
99.4%
1 1065
 
0.6%

Length

2025-09-15T10:31:28.106300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:28.209276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176772
99.4%
1 1065
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176772
99.4%
1 1065
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176772
99.4%
1 1065
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176772
99.4%
1 1065
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176772
99.4%
1 1065
 
0.6%

location_satara
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177778 
1
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Length

2025-09-15T10:31:28.320110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:28.421316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

location_shimla
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177778 
1
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Length

2025-09-15T10:31:28.514217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:28.628888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177778
> 99.9%
1 59
 
< 0.1%

location_siliguri
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177449 
1
 
388

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%

Length

2025-09-15T10:31:28.731006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:28.824888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177449
99.8%
1 388
 
0.2%

location_solapur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177807 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Length

2025-09-15T10:31:28.935753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:29.044617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177807
> 99.9%
1 30
 
< 0.1%

location_sonipat
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177089 
1
 
748

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177089
99.6%
1 748
 
0.4%

Length

2025-09-15T10:31:29.137889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:29.256605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177089
99.6%
1 748
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 177089
99.6%
1 748
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177089
99.6%
1 748
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177089
99.6%
1 748
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177089
99.6%
1 748
 
0.4%

location_surat
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
175657 
1
 
2180

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175657
98.8%
1 2180
 
1.2%

Length

2025-09-15T10:31:29.357357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:29.450409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 175657
98.8%
1 2180
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 175657
98.8%
1 2180
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 175657
98.8%
1 2180
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 175657
98.8%
1 2180
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 175657
98.8%
1 2180
 
1.2%

location_thane
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
175968 
1
 
1869

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 175968
98.9%
1 1869
 
1.1%

Length

2025-09-15T10:31:29.559225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:29.669253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 175968
98.9%
1 1869
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 175968
98.9%
1 1869
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 175968
98.9%
1 1869
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 175968
98.9%
1 1869
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 175968
98.9%
1 1869
 
1.1%

location_thrissur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177631 
1
 
206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177631
99.9%
1 206
 
0.1%

Length

2025-09-15T10:31:29.761509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:29.871276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177631
99.9%
1 206
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177631
99.9%
1 206
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177631
99.9%
1 206
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177631
99.9%
1 206
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177631
99.9%
1 206
 
0.1%

location_tirupati
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177747 
1
 
90

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

Length

2025-09-15T10:31:29.981079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:30.073941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177747
99.9%
1 90
 
0.1%

location_trichy
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177718 
1
 
119

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Length

2025-09-15T10:31:30.183874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:30.293195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

location_trivandrum
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177632 
1
 
205

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177632
99.9%
1 205
 
0.1%

Length

2025-09-15T10:31:30.402778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:30.501955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177632
99.9%
1 205
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177632
99.9%
1 205
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177632
99.9%
1 205
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177632
99.9%
1 205
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177632
99.9%
1 205
 
0.1%

location_udaipur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177718 
1
 
119

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Length

2025-09-15T10:31:30.761502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:30.856770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177718
99.9%
1 119
 
0.1%

location_udupi
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177777 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Length

2025-09-15T10:31:30.965426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:31.058653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

location_vadodara
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
175477 
1
 
2360

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175477
98.7%
1 2360
 
1.3%

Length

2025-09-15T10:31:31.167831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:31.277441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 175477
98.7%
1 2360
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 175477
98.7%
1 2360
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 175477
98.7%
1 2360
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 175477
98.7%
1 2360
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 175477
98.7%
1 2360
 
1.3%

location_vapi
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177688 
1
 
149

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177688
99.9%
1 149
 
0.1%

Length

2025-09-15T10:31:31.386498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:31.490082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177688
99.9%
1 149
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177688
99.9%
1 149
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177688
99.9%
1 149
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177688
99.9%
1 149
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177688
99.9%
1 149
 
0.1%

location_varanasi
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177571 
1
 
266

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177571
99.9%
1 266
 
0.1%

Length

2025-09-15T10:31:31.594844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:31.685336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177571
99.9%
1 266
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 177571
99.9%
1 266
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177571
99.9%
1 266
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177571
99.9%
1 266
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177571
99.9%
1 266
 
0.1%

location_vijayawada
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177285 
1
 
552

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177285
99.7%
1 552
 
0.3%

Length

2025-09-15T10:31:31.801414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:31.905781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177285
99.7%
1 552
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 177285
99.7%
1 552
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177285
99.7%
1 552
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177285
99.7%
1 552
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177285
99.7%
1 552
 
0.3%

location_visakhapatnam
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176108 
1
 
1729

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176108
99.0%
1 1729
 
1.0%

Length

2025-09-15T10:31:31.997333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:32.107663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176108
99.0%
1 1729
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 176108
99.0%
1 1729
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176108
99.0%
1 1729
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176108
99.0%
1 1729
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176108
99.0%
1 1729
 
1.0%

location_vrindavan
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177777 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Length

2025-09-15T10:31:32.216986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:32.310017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177777
> 99.9%
1 60
 
< 0.1%

location_zirakpur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176359 
1
 
1478

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176359
99.2%
1 1478
 
0.8%

Length

2025-09-15T10:31:32.418960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:32.528434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176359
99.2%
1 1478
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 176359
99.2%
1 1478
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176359
99.2%
1 1478
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176359
99.2%
1 1478
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176359
99.2%
1 1478
 
0.8%

Transaction_New Property
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
136396 
1
41441 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 136396
76.7%
1 41441
 
23.3%

Length

2025-09-15T10:31:32.622266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:32.730741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 136396
76.7%
1 41441
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 136396
76.7%
1 41441
 
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 136396
76.7%
1 41441
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 136396
76.7%
1 41441
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 136396
76.7%
1 41441
 
23.3%

Transaction_Other
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177134 
1
 
703

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177134
99.6%
1 703
 
0.4%

Length

2025-09-15T10:31:32.840867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:32.950256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177134
99.6%
1 703
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 177134
99.6%
1 703
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177134
99.6%
1 703
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177134
99.6%
1 703
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177134
99.6%
1 703
 
0.4%

Transaction_Rent/Lease
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
177835 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 177835
> 99.9%
1 2
 
< 0.1%

Length

2025-09-15T10:31:33.059698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:33.152732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 177835
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 177835
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177835
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177835
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177835
> 99.9%
1 2
 
< 0.1%

Transaction_Resale
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
135691 
0
42146 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 135691
76.3%
0 42146
 
23.7%

Length

2025-09-15T10:31:33.273905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:33.371387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 135691
76.3%
0 42146
 
23.7%

Most occurring characters

ValueCountFrequency (%)
1 135691
76.3%
0 42146
 
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 135691
76.3%
0 42146
 
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 135691
76.3%
0 42146
 
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 135691
76.3%
0 42146
 
23.7%

Furnishing_Furnished
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
158417 
1
19420 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 158417
89.1%
1 19420
 
10.9%

Length

2025-09-15T10:31:33.481296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:33.590276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 158417
89.1%
1 19420
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0 158417
89.1%
1 19420
 
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 158417
89.1%
1 19420
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 158417
89.1%
1 19420
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 158417
89.1%
1 19420
 
10.9%

Furnishing_Semi-Furnished
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
92907 
1
84930 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 92907
52.2%
1 84930
47.8%

Length

2025-09-15T10:31:33.684104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:33.793790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 92907
52.2%
1 84930
47.8%

Most occurring characters

ValueCountFrequency (%)
0 92907
52.2%
1 84930
47.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 92907
52.2%
1 84930
47.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 92907
52.2%
1 84930
47.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 92907
52.2%
1 84930
47.8%

Furnishing_Unfurnished
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
104350 
1
73487 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 104350
58.7%
1 73487
41.3%

Length

2025-09-15T10:31:33.902234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:34.012818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 104350
58.7%
1 73487
41.3%

Most occurring characters

ValueCountFrequency (%)
0 104350
58.7%
1 73487
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 104350
58.7%
1 73487
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 104350
58.7%
1 73487
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 104350
58.7%
1 73487
41.3%

facing_East
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
117974 
0
59863 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 117974
66.3%
0 59863
33.7%

Length

2025-09-15T10:31:34.121386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:34.230736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 117974
66.3%
0 59863
33.7%

Most occurring characters

ValueCountFrequency (%)
1 117974
66.3%
0 59863
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 117974
66.3%
0 59863
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 117974
66.3%
0 59863
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 117974
66.3%
0 59863
33.7%

facing_North
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
162537 
1
 
15300

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 162537
91.4%
1 15300
 
8.6%

Length

2025-09-15T10:31:34.340176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:34.448866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 162537
91.4%
1 15300
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 162537
91.4%
1 15300
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 162537
91.4%
1 15300
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 162537
91.4%
1 15300
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 162537
91.4%
1 15300
 
8.6%

facing_North - East
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
154490 
1
23347 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154490
86.9%
1 23347
 
13.1%

Length

2025-09-15T10:31:34.558884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:34.667537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 154490
86.9%
1 23347
 
13.1%

Most occurring characters

ValueCountFrequency (%)
0 154490
86.9%
1 23347
 
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154490
86.9%
1 23347
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154490
86.9%
1 23347
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154490
86.9%
1 23347
 
13.1%

facing_North - West
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
174034 
1
 
3803

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174034
97.9%
1 3803
 
2.1%

Length

2025-09-15T10:31:34.784134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:34.887042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 174034
97.9%
1 3803
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 174034
97.9%
1 3803
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 174034
97.9%
1 3803
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 174034
97.9%
1 3803
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 174034
97.9%
1 3803
 
2.1%

facing_South
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
173521 
1
 
4316

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173521
97.6%
1 4316
 
2.4%

Length

2025-09-15T10:31:34.996203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:35.103383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 173521
97.6%
1 4316
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 173521
97.6%
1 4316
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 173521
97.6%
1 4316
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 173521
97.6%
1 4316
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 173521
97.6%
1 4316
 
2.4%

facing_South - East
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
175266 
1
 
2571

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175266
98.6%
1 2571
 
1.4%

Length

2025-09-15T10:31:35.210587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:35.302670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 175266
98.6%
1 2571
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 175266
98.6%
1 2571
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 175266
98.6%
1 2571
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 175266
98.6%
1 2571
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 175266
98.6%
1 2571
 
1.4%

facing_South -West
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
175785 
1
 
2052

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175785
98.8%
1 2052
 
1.2%

Length

2025-09-15T10:31:35.421103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:35.521971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 175785
98.8%
1 2052
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 175785
98.8%
1 2052
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 175785
98.8%
1 2052
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 175785
98.8%
1 2052
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 175785
98.8%
1 2052
 
1.2%

facing_West
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
169363 
1
 
8474

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 169363
95.2%
1 8474
 
4.8%

Length

2025-09-15T10:31:35.622540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:35.870857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 169363
95.2%
1 8474
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 169363
95.2%
1 8474
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 169363
95.2%
1 8474
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 169363
95.2%
1 8474
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 169363
95.2%
1 8474
 
4.8%

Ownership_Co-operative Society
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
174488 
1
 
3349

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 174488
98.1%
1 3349
 
1.9%

Length

2025-09-15T10:31:35.980964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:36.090074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 174488
98.1%
1 3349
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 174488
98.1%
1 3349
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 174488
98.1%
1 3349
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 174488
98.1%
1 3349
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 174488
98.1%
1 3349
 
1.9%

Ownership_Freehold
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
168326 
0
 
9511

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 168326
94.7%
0 9511
 
5.3%

Length

2025-09-15T10:31:36.198551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:36.300712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 168326
94.7%
0 9511
 
5.3%

Most occurring characters

ValueCountFrequency (%)
1 168326
94.7%
0 9511
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 168326
94.7%
0 9511
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 168326
94.7%
0 9511
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 168326
94.7%
0 9511
 
5.3%

Ownership_Leasehold
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
172688 
1
 
5149

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172688
97.1%
1 5149
 
2.9%

Length

2025-09-15T10:31:36.406885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:36.511932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 172688
97.1%
1 5149
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 172688
97.1%
1 5149
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 172688
97.1%
1 5149
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 172688
97.1%
1 5149
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 172688
97.1%
1 5149
 
2.9%

Ownership_Power Of Attorney
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
176824 
1
 
1013

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177837
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176824
99.4%
1 1013
 
0.6%

Length

2025-09-15T10:31:36.617817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T10:31:36.717958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176824
99.4%
1 1013
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176824
99.4%
1 1013
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176824
99.4%
1 1013
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176824
99.4%
1 1013
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176824
99.4%
1 1013
 
0.6%

Interactions

2025-09-15T10:31:09.491692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:06.066818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:06.726146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:07.440592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:08.122659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:08.799571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:09.604712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:06.172127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:06.837473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:07.552014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:08.228801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:08.908076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:09.725508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:06.285082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:06.956006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:07.669151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:08.347192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:09.026460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:09.837500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:06.391951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:07.070417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:07.778486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:08.460104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:09.139688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:09.950824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:06.497374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:07.179851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:07.883593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:08.565506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:09.249305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:10.074708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:06.610207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:07.303442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:08.005672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:08.682174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-15T10:31:09.370597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-09-15T10:31:36.919243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Amount(in rupees)AreaBHKBalconyBathroomFloorFurnishing_FurnishedFurnishing_Semi-FurnishedFurnishing_UnfurnishedOwnership_Co-operative SocietyOwnership_FreeholdOwnership_LeaseholdOwnership_Power Of AttorneyTransaction_New PropertyTransaction_OtherTransaction_Rent/LeaseTransaction_Resalefacing_Eastfacing_Northfacing_North - Eastfacing_North - Westfacing_Southfacing_South - Eastfacing_South -Westfacing_Westlocation_agralocation_ahmadnagarlocation_ahmedabadlocation_allahabadlocation_aurangabadlocation_badlapurlocation_bangalorelocation_belgaumlocation_bhiwadilocation_bhiwandilocation_bhopallocation_bhubaneswarlocation_chandigarhlocation_chennailocation_coimbatorelocation_dehradunlocation_durgapurlocation_ernakulamlocation_faridabadlocation_ghaziabadlocation_goalocation_greater-noidalocation_gunturlocation_gurgaonlocation_guwahatilocation_gwaliorlocation_haridwarlocation_hyderabadlocation_indorelocation_jabalpurlocation_jaipurlocation_jamshedpurlocation_jodhpurlocation_kalyanlocation_kanpurlocation_kochilocation_kolkatalocation_kozhikodelocation_lucknowlocation_ludhianalocation_madurailocation_mangalorelocation_mohalilocation_mumbailocation_mysorelocation_nagpurlocation_nashiklocation_navi-mumbailocation_navsarilocation_nellorelocation_new-delhilocation_noidalocation_palakkadlocation_palgharlocation_panchkulalocation_patnalocation_pondicherrylocation_punelocation_raipurlocation_rajahmundrylocation_ranchilocation_sataralocation_shimlalocation_siligurilocation_solapurlocation_sonipatlocation_suratlocation_thanelocation_thrissurlocation_tirupatilocation_trichylocation_trivandrumlocation_udaipurlocation_udupilocation_vadodaralocation_vapilocation_varanasilocation_vijayawadalocation_visakhapatnamlocation_vrindavanlocation_zirakpur
Amount(in rupees)1.0000.7170.6640.3510.6940.2780.0420.1400.1250.0450.0510.0520.0860.1760.0820.0000.1730.1880.0820.2350.0480.0970.0450.0450.0580.0420.0210.0890.0120.0550.0800.2610.0420.1640.0200.0420.0240.0470.0990.0390.0280.0420.0120.0550.0260.0340.0720.0320.2980.0500.0270.0590.1880.0130.0290.1800.0530.0200.0530.0340.0200.2180.0090.0300.0070.0090.0290.0330.1480.0160.0420.0330.0160.0280.0090.3120.0270.0080.1220.0330.0380.0070.0190.0640.0170.0630.0350.0070.0410.0130.0430.0470.0210.0160.0300.0220.0230.0260.0160.0990.1040.0310.0430.0640.0340.055
Area0.7171.0000.8890.3800.8020.2740.0000.0010.0000.0000.0000.0000.0000.0020.0000.0000.0020.0000.0000.0040.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0050.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
BHK0.6640.8891.0000.3310.8100.2490.0200.0070.0040.0000.0120.0170.0000.0020.0000.0000.0020.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0090.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Balcony0.3510.3800.3311.0000.3270.2250.0610.2110.1820.0140.1870.2320.1040.0460.0570.0000.0440.2650.0970.3810.0800.0890.0570.0470.0570.0070.0040.1180.0000.0150.0150.1290.0000.0200.0080.0080.0130.0810.0620.0210.0120.0160.0050.0720.0270.0210.1550.0250.2630.0220.0060.0080.1300.0150.0030.0530.0230.0030.0080.0110.0150.2240.0080.0290.0220.0030.0130.0510.0430.0100.0160.0110.0250.0000.0060.1770.1230.0040.0120.0760.0180.0000.0290.0170.0060.0390.0080.0050.0400.0020.0760.0430.0240.0110.0050.0110.0080.0000.0000.0170.0120.0040.0210.0320.0070.064
Bathroom0.6940.8020.8100.3271.0000.2500.0500.1520.1510.1240.0670.0860.0580.1700.0570.0000.1640.1610.0530.1750.0490.1000.0290.0390.0520.0090.0280.0840.0140.0610.0820.1770.0110.0500.0270.0130.0140.0630.0910.0210.0130.0180.0050.0400.0130.0460.0290.0160.3270.0360.0110.0330.0920.0110.0130.0640.0420.0040.0960.0270.0430.1770.0020.0060.0030.0120.0160.0270.0570.0090.0180.0210.0490.0060.0010.1400.0160.0000.0920.0530.0410.0100.0620.0150.0110.0490.0310.0070.0260.0120.0270.0220.0900.0100.0090.0110.0090.0090.0080.0170.0320.0220.0220.0460.0380.062
Floor0.2780.2740.2490.2250.2501.0000.0260.0670.0550.0810.1360.1570.0280.0860.0330.0000.0910.1270.0300.1710.0370.0850.0370.0650.0370.0120.0020.0320.0030.0240.0100.1480.0060.0420.0000.0060.0100.0170.0490.0270.0250.0050.0000.0160.0350.0360.1910.0200.1600.0180.0050.0110.0690.0000.0110.0800.0240.0000.0080.0200.0480.0420.0040.0230.0000.0000.0180.0090.1690.0110.0270.0110.0250.0000.0000.1000.0810.0000.0240.0140.0270.0020.0410.0170.0110.0310.0080.0070.0220.0000.0280.0620.0830.0000.0100.0110.0110.0000.0030.0370.0070.0100.0300.0460.0000.019
Furnishing_Furnished0.0420.0000.0200.0610.0500.0261.0000.3350.2940.0000.0300.0350.0130.1030.1690.0000.0770.0310.0570.0250.0380.0320.0260.2030.0070.0060.0000.0450.0010.0000.0050.0810.0020.0030.0000.0000.0030.0120.0250.0000.0050.0000.0120.0270.0070.0450.0220.0330.0740.0020.0030.0050.0130.0000.0000.0070.0000.0050.0000.0210.0210.1030.0090.0040.0080.0010.0000.0040.0070.0020.0000.0000.0050.0020.0040.0000.0050.0020.0070.0060.0000.0000.0040.0000.0040.0000.0030.0170.0000.0000.0040.0350.0030.0080.0000.0100.0100.0100.0000.0450.0050.0170.0260.0130.0140.009
Furnishing_Semi-Furnished0.1400.0010.0070.2110.1520.0670.3351.0000.8020.0580.0170.0520.0420.0270.0550.0000.0350.0040.0170.1050.0210.0490.0090.0790.0370.0040.0060.1080.0000.0210.0180.0720.0000.0050.0060.0040.0150.0490.0500.0050.0210.0100.0000.0530.0230.0370.0270.0160.0600.0260.0010.0040.0600.0070.0020.0740.0170.0000.0120.0000.0140.1100.0050.0050.0070.0000.0180.0400.0240.0000.0110.0200.0250.0050.0020.0730.0270.0010.0170.0040.0150.0000.0400.0180.0080.0160.0030.0050.0260.0020.0000.0590.0240.0000.0030.0050.0030.0000.0000.0270.0070.0100.0080.0440.0110.055
Furnishing_Unfurnished0.1250.0000.0040.1820.1510.0550.2940.8021.0000.0590.0000.0310.0340.0930.0510.0000.0840.0150.0530.0900.0460.0700.0260.0480.0330.0080.0070.0810.0000.0200.0220.0220.0000.0010.0060.0060.0130.0420.0350.0050.0250.0090.0060.0360.0290.0090.0130.0030.0140.0280.0000.0000.0700.0070.0030.0700.0180.0000.0110.0130.0000.0460.0000.0020.0000.0000.0180.0440.0190.0000.0110.0190.0290.0030.0000.0750.0240.0000.0220.0090.0160.0000.0430.0170.0050.0150.0060.0040.0280.0000.0040.0380.0260.0020.0000.0000.0010.0050.0000.0000.0040.0000.0070.0360.0000.050
Ownership_Co-operative Society0.0450.0000.0000.0140.1240.0810.0000.0580.0591.0000.5830.0240.0100.0250.0080.0000.0240.0150.0070.0180.0060.0030.0000.0020.0110.0020.0000.0090.0000.0120.0690.0520.0000.0000.0130.0000.0000.0090.0320.0020.0070.0020.0010.0150.0020.0250.0000.0010.0300.0080.0000.0000.0290.0010.0010.0250.0060.0000.0820.0060.0060.0440.0000.0000.0020.0000.0050.0000.1580.0000.0060.0110.0810.0090.0000.0550.0030.0020.0640.0000.0030.0000.1330.0020.0000.0050.0050.0000.0060.0000.0060.0520.1020.0020.0000.0010.0000.0050.0070.0270.0140.0000.0050.0050.0000.003
Ownership_Freehold0.0510.0000.0120.1870.0670.1360.0300.0170.0000.5831.0000.7260.3180.0170.0150.0000.0150.0920.0220.1070.0170.0050.0010.0000.0590.0070.0000.0140.0040.0020.0360.0890.0000.0450.0050.0000.0030.0130.0540.0080.0050.0000.0050.0280.0000.0040.3850.0040.0590.0050.0030.0000.0540.0040.0000.0150.0100.0000.0410.0070.0140.0790.0000.0050.0050.0000.0010.0130.0940.0030.0000.0000.0520.0090.0000.0890.2190.0030.0320.0070.0080.0000.0740.0020.0030.0110.0000.0030.0080.0000.0080.0210.0540.0040.0030.0040.0020.0020.0020.0040.0050.0030.0080.0060.0030.014
Ownership_Leasehold0.0520.0000.0170.2320.0860.1570.0350.0520.0310.0240.7261.0000.0130.0330.0100.0000.0340.1420.0240.1650.0300.0050.0010.0000.0710.0040.0000.0020.0030.0040.0050.0660.0000.0630.0020.0000.0030.0060.0410.0080.0000.0030.0040.0230.0000.0120.5170.0050.0030.0090.0020.0000.0430.0010.0000.0060.0020.0000.0090.0090.0120.0620.0000.0060.0040.0000.0070.0140.0000.0040.0020.0050.0060.0000.0000.0680.2930.0000.0080.0070.0070.0000.0070.0060.0020.0090.0000.0000.0040.0000.0090.0160.0140.0050.0000.0030.0040.0000.0000.0150.0020.0040.0060.0140.0000.014
Ownership_Power Of Attorney0.0860.0000.0000.1040.0580.0280.0130.0420.0340.0100.3180.0131.0000.0790.0030.0000.0780.0130.0000.0150.0030.0030.0000.0040.0000.0020.0000.0190.0000.0000.0010.0260.0000.0000.0000.0000.0000.0050.0110.0000.0000.0000.0000.0030.0000.0030.0050.0030.1130.0510.0000.0000.0130.0000.0000.0150.0110.0000.0010.0090.0000.0200.0000.0000.0000.0000.0000.0040.0020.0000.0000.0000.0030.0070.0000.0170.0020.0070.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0060.0030.0080.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.002
Transaction_New Property0.1760.0020.0020.0460.1700.0860.1030.0270.0930.0250.0170.0330.0791.0000.0350.0000.9890.0760.0400.1090.0460.0000.0320.0360.0110.0130.0040.0750.0070.0210.0180.0430.0060.0240.0050.0050.0000.0700.0240.0150.0170.0000.0150.0400.0240.0160.0150.0000.1050.0310.0100.0000.0490.0030.0120.1600.0060.0070.0260.0080.0230.0790.0000.0030.0000.0060.0090.0230.0000.0000.0050.0000.0230.0000.0050.0300.0200.0010.0190.0150.0030.0040.0190.0070.0080.0040.0070.0000.0230.0050.0100.0030.0220.0140.0080.0120.0080.0100.0090.0320.0130.0000.0200.0040.0000.051
Transaction_Other0.0820.0000.0000.0570.0570.0330.1690.0550.0510.0080.0150.0100.0030.0351.0000.0000.1130.0450.0190.0240.0090.0090.0070.0060.0130.0000.0000.0140.0000.0000.0000.0230.0000.0040.0000.0000.0000.0050.0130.0020.0030.0000.0000.0080.0030.0040.0080.0000.0210.0020.0000.0000.0130.0000.0000.0130.0020.0000.0000.0020.0030.1570.0000.0030.0000.0000.0000.0050.0000.0000.0020.0000.0020.0000.0000.0240.0030.0000.0010.0000.0030.0000.0010.0010.0000.0040.0000.0000.0000.0000.0020.0060.0060.0000.0000.0000.0000.0000.0000.0060.0000.0000.0010.0050.0000.005
Transaction_Rent/Lease0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0030.0020.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Transaction_Resale0.1730.0020.0020.0440.1640.0910.0770.0350.0840.0240.0150.0340.0780.9890.1130.0031.0000.0690.0370.1050.0470.0020.0330.0370.0090.0130.0040.0770.0070.0220.0180.0460.0070.0250.0060.0050.0000.0690.0260.0160.0160.0000.0150.0380.0230.0170.0140.0000.1010.0300.0110.0000.0470.0030.0120.1570.0070.0070.0260.0080.0240.0550.0000.0030.0000.0060.0100.0220.0000.0000.0040.0000.0240.0000.0050.0330.0200.0010.0190.0150.0030.0040.0190.0060.0080.0040.0070.0010.0220.0050.0110.0020.0230.0140.0080.0120.0080.0100.0100.0330.0130.0000.0210.0050.0000.050
facing_East0.1880.0000.0000.2650.1610.1270.0310.0040.0150.0150.0920.1420.0130.0760.0450.0020.0691.0000.4310.5460.2070.2210.1700.1520.3140.0120.0050.1600.0050.0190.0190.1020.0060.0100.0050.0120.0040.0300.1010.0080.0050.0060.0100.0280.0050.0160.0600.0050.0390.0050.0150.0060.0220.0070.0130.0020.0170.0040.0150.0000.0320.0730.0000.0050.0080.0000.0090.0220.0080.0050.0150.0120.0180.0030.0020.1010.0250.0050.0200.0190.0150.0020.0500.0200.0030.0030.0080.0060.0050.0050.0360.0360.0260.0070.0070.0000.0130.0060.0030.0400.0050.0160.0160.0010.0090.028
facing_North0.0820.0000.0000.0970.0530.0300.0570.0170.0530.0070.0220.0240.0000.0400.0190.0000.0370.4311.0000.1190.0450.0480.0370.0330.0690.0000.0000.0750.0000.0030.0050.1070.0000.0060.0010.0030.0030.0040.1500.0080.0080.0040.0040.0680.0120.0060.0310.0000.0430.0070.0070.0040.0390.0030.0060.0250.0050.0000.0070.0000.0100.0690.0000.0040.0000.0000.0020.0140.0050.0000.0040.0070.0120.0000.0000.0600.0110.0000.0090.0080.0060.0000.0210.0040.0000.0100.0030.0030.0070.0000.0480.0250.0100.0020.0000.0040.0060.0000.0000.0200.0000.0040.0040.0190.0000.000
facing_North - East0.2350.0040.0000.3810.1750.1710.0250.1050.0900.0180.1070.1650.0150.1090.0240.0000.1050.5460.1191.0000.0570.0610.0470.0420.0870.0140.0000.0960.0070.0180.0150.1440.0050.0010.0020.0070.0080.0690.0720.0200.0000.0030.0070.0310.0260.0030.1100.0110.1300.0080.0080.0030.0960.0090.0080.0560.0120.0060.0120.0000.0210.0310.0060.0000.0060.0030.0090.0470.0100.0100.0180.0110.0000.0000.0020.1990.0560.0040.0150.0300.0180.0020.0340.0160.0050.0080.0040.0020.0000.0040.0080.0240.0120.0070.0070.0080.0090.0060.0000.0320.0050.0090.0150.0310.0040.066
facing_North - West0.0480.0000.0000.0800.0490.0370.0380.0210.0460.0060.0170.0300.0030.0460.0090.0000.0470.2070.0450.0571.0000.0230.0180.0160.0330.0000.0000.0370.0000.0020.0030.0540.0000.0000.0000.0020.0030.0020.0340.0060.0090.0000.0000.0120.0000.0030.0280.0040.0350.0060.0030.0000.0330.0000.0000.0250.0040.0000.0030.0020.0080.0460.0200.0030.0000.0000.0000.0140.0000.0000.0080.0020.0090.0000.0000.2290.0030.0000.0000.0000.0000.0000.0120.0030.0010.0070.0000.0000.0030.0000.0060.0090.0090.0000.0010.0000.0000.0020.0000.0110.0000.0000.0050.0110.0000.007
facing_South0.0970.0000.0100.0890.1000.0850.0320.0490.0700.0030.0050.0050.0030.0000.0090.0040.0020.2210.0480.0610.0231.0000.0190.0170.0350.0060.0000.0400.0000.0040.0040.0570.0000.0100.0010.0000.0030.0130.1150.0160.0050.0100.0000.0090.0070.0060.0100.0030.0220.0000.0030.0040.0360.0060.0020.0290.0000.0000.0000.0050.0070.1400.0000.0070.0000.0080.0050.0060.0060.0000.0050.0000.0080.0000.0000.0590.0090.0000.0000.0060.0000.0050.0120.0000.0000.0040.0000.0000.0090.0000.0020.0080.0090.0010.0000.0050.0000.0000.0000.0080.0000.0040.0060.0280.0000.012
facing_South - East0.0450.0000.0000.0570.0290.0370.0260.0090.0260.0000.0010.0010.0000.0320.0070.0000.0330.1700.0370.0470.0180.0191.0000.0130.0270.0000.0000.0290.0000.0030.0020.0450.0000.0000.0000.0000.0000.0060.0260.0040.0100.0270.0000.0040.0000.0060.0110.0030.0320.0000.0000.0000.0300.0000.0000.0190.0010.0000.0000.0000.0050.2110.0000.0000.0000.0000.0000.0030.0070.0000.0050.0000.0050.0000.0000.0410.0000.0000.0000.0000.0080.0000.0080.0030.0000.0130.0000.0040.0000.0000.0000.0000.0040.0000.0000.0020.0000.0000.0000.0050.0000.0030.0050.0080.0000.004
facing_South -West0.0450.0000.0000.0470.0390.0650.2030.0790.0480.0020.0000.0000.0040.0360.0060.0000.0370.1520.0330.0420.0160.0170.0131.0000.0240.0010.0000.0260.0000.0030.0000.0700.0000.0040.0000.0000.0000.0080.0240.0030.0110.0200.0000.0000.0000.0030.0090.0030.0330.0010.0000.0010.0270.0000.0000.0180.0020.0000.0000.0000.0050.0870.0000.0040.0000.0000.0030.0000.0040.0000.0050.0000.0000.0000.0000.0390.0010.0000.0020.0030.0000.0000.0100.0030.0000.0000.0000.0000.0000.0000.0000.0310.0030.0020.0000.0000.0000.0000.0000.0030.0000.0030.0050.0080.0000.007
facing_West0.0580.0000.0000.0570.0520.0370.0070.0370.0330.0110.0590.0710.0000.0110.0130.0000.0090.3140.0690.0870.0330.0350.0270.0241.0000.0020.0000.0180.0000.0000.0030.0700.0000.0080.0000.0020.0020.0170.1060.0000.0010.0030.0000.0210.0050.0070.0140.0170.0210.0050.0020.0040.1310.0020.0000.1070.0050.0000.0020.0020.0060.0280.0020.0040.0020.0000.0060.0120.0120.0020.0050.0080.0070.0000.0000.0830.0140.0000.0030.0060.0000.0000.0000.0010.0040.0070.0000.0020.0030.0000.0020.0090.0060.0020.0030.0000.0000.0000.0000.0060.0000.0030.0110.0270.0000.018
location_agra0.0420.0500.0000.0070.0090.0120.0060.0040.0080.0020.0070.0040.0020.0130.0000.0000.0130.0120.0000.0140.0000.0060.0000.0010.0021.0000.0000.0130.0000.0000.0000.0190.0000.0020.0000.0000.0000.0030.0120.0000.0020.0000.0000.0070.0010.0020.0070.0000.0170.0000.0000.0000.0120.0000.0000.0100.0000.0000.0000.0000.0020.0180.0000.0010.0000.0000.0000.0030.0040.0000.0000.0000.0000.0000.0000.0200.0030.0000.0000.0000.0010.0000.0040.0000.0000.0020.0000.0000.0000.0000.0000.0040.0040.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0040.0000.003
location_ahmadnagar0.0210.0000.0000.0040.0280.0020.0000.0060.0070.0000.0000.0000.0000.0040.0000.0000.0040.0050.0000.0000.0000.0000.0000.0000.0000.0001.0000.0010.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_ahmedabad0.0890.0000.0000.1180.0840.0320.0450.1080.0810.0090.0140.0020.0190.0750.0140.0000.0770.1600.0750.0960.0370.0400.0290.0260.0180.0130.0011.0000.0080.0130.0120.1070.0040.0210.0050.0060.0130.0240.0680.0160.0200.0070.0080.0400.0190.0220.0440.0100.0950.0180.0070.0080.0710.0060.0060.0590.0160.0040.0160.0180.0210.1030.0040.0180.0070.0010.0130.0250.0280.0080.0160.0100.0170.0010.0010.1120.0230.0010.0140.0110.0180.0010.0310.0140.0050.0210.0040.0040.0120.0010.0180.0310.0280.0090.0050.0060.0090.0060.0040.0320.0070.0100.0150.0270.0040.025
location_allahabad0.0120.0000.0000.0000.0140.0030.0010.0000.0000.0000.0040.0030.0000.0070.0000.0000.0070.0050.0000.0070.0000.0000.0000.0000.0000.0000.0000.0081.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0030.0000.0000.0040.0000.0100.0000.0000.0000.0070.0000.0000.0060.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.000
location_aurangabad0.0550.0000.0000.0150.0610.0240.0000.0210.0200.0120.0020.0040.0000.0210.0000.0000.0220.0190.0030.0180.0020.0040.0030.0030.0000.0000.0000.0130.0001.0000.0000.0190.0000.0020.0000.0000.0000.0030.0120.0000.0020.0000.0000.0070.0010.0020.0070.0000.0170.0000.0000.0000.0120.0000.0000.0100.0000.0000.0000.0000.0020.0180.0000.0010.0000.0000.0000.0030.0040.0000.0000.0000.0000.0000.0000.0200.0030.0000.0000.0000.0010.0000.0040.0000.0000.0020.0000.0000.0000.0000.0000.0040.0040.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0040.0000.003
location_badlapur0.0800.0000.0000.0150.0820.0100.0050.0180.0220.0690.0360.0050.0010.0180.0000.0000.0180.0190.0050.0150.0030.0040.0020.0000.0030.0000.0000.0120.0000.0001.0000.0180.0000.0010.0000.0000.0000.0030.0110.0000.0010.0000.0000.0060.0010.0020.0070.0000.0160.0000.0000.0000.0120.0000.0000.0090.0000.0000.0000.0000.0010.0170.0000.0000.0000.0000.0000.0030.0030.0000.0000.0000.0000.0000.0000.0190.0020.0000.0000.0000.0000.0000.0040.0000.0000.0020.0000.0000.0000.0000.0000.0040.0040.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0030.0000.003
location_bangalore0.2610.0000.0000.1290.1770.1480.0810.0720.0220.0520.0890.0660.0260.0430.0230.0000.0460.1020.1070.1440.0540.0570.0450.0700.0700.0190.0040.1070.0120.0190.0181.0000.0060.0290.0080.0090.0180.0340.0950.0220.0280.0110.0120.0570.0270.0310.0620.0150.1330.0250.0110.0120.1000.0090.0090.0830.0230.0060.0220.0250.0290.1440.0060.0260.0110.0030.0190.0350.0390.0120.0230.0140.0240.0040.0040.1570.0320.0040.0200.0160.0250.0040.0430.0200.0080.0300.0060.0060.0180.0040.0250.0430.0400.0130.0080.0090.0130.0090.0060.0450.0110.0150.0210.0380.0060.035
location_belgaum0.0420.0000.0000.0000.0110.0060.0020.0000.0000.0000.0000.0000.0000.0060.0000.0000.0070.0060.0000.0050.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0061.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0030.0000.0000.0020.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_bhiwadi0.1640.0310.0000.0200.0500.0420.0030.0050.0010.0000.0450.0630.0000.0240.0040.0000.0250.0100.0060.0010.0000.0100.0000.0040.0080.0020.0000.0210.0000.0020.0010.0290.0001.0000.0000.0000.0020.0060.0190.0030.0050.0000.0000.0110.0040.0050.0120.0000.0260.0040.0000.0000.0190.0000.0000.0160.0030.0000.0030.0040.0050.0280.0000.0040.0000.0000.0020.0060.0070.0000.0030.0000.0030.0000.0000.0310.0050.0000.0020.0010.0040.0000.0080.0020.0000.0050.0000.0000.0010.0000.0040.0080.0070.0000.0000.0000.0000.0000.0000.0080.0000.0000.0030.0070.0000.006
location_bhiwandi0.0200.0000.0000.0080.0270.0000.0000.0060.0060.0130.0050.0020.0000.0050.0000.0000.0060.0050.0010.0020.0000.0010.0000.0000.0000.0000.0000.0050.0000.0000.0000.0080.0000.0001.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0010.0000.0070.0000.0000.0000.0050.0000.0000.0030.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_bhopal0.0420.0000.0000.0080.0130.0060.0000.0040.0060.0000.0000.0000.0000.0050.0000.0000.0050.0120.0030.0070.0020.0000.0000.0000.0020.0000.0000.0060.0000.0000.0000.0090.0000.0000.0001.0000.0000.0000.0050.0000.0000.0000.0000.0020.0000.0000.0030.0000.0080.0000.0000.0000.0060.0000.0000.0040.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_bhubaneswar0.0240.0000.0000.0130.0140.0100.0030.0150.0130.0000.0030.0030.0000.0000.0000.0000.0000.0040.0030.0080.0030.0030.0000.0000.0020.0000.0000.0130.0000.0000.0000.0180.0000.0020.0000.0001.0000.0030.0110.0000.0010.0000.0000.0060.0010.0020.0070.0000.0160.0000.0000.0000.0120.0000.0000.0100.0000.0000.0000.0000.0010.0170.0000.0000.0000.0000.0000.0030.0030.0000.0000.0000.0000.0000.0000.0190.0020.0000.0000.0000.0000.0000.0040.0000.0000.0020.0000.0000.0000.0000.0000.0040.0040.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0030.0000.003
location_chandigarh0.0470.0000.0000.0810.0630.0170.0120.0490.0420.0090.0130.0060.0050.0700.0050.0000.0690.0300.0040.0690.0020.0130.0060.0080.0170.0030.0000.0240.0000.0030.0030.0340.0000.0060.0000.0000.0031.0000.0220.0040.0060.0000.0000.0130.0050.0060.0140.0010.0300.0050.0000.0000.0230.0000.0000.0190.0040.0000.0040.0050.0060.0330.0000.0050.0000.0000.0030.0070.0080.0000.0040.0010.0040.0000.0000.0360.0070.0000.0030.0020.0050.0000.0090.0030.0000.0060.0000.0000.0030.0000.0050.0090.0090.0000.0000.0000.0000.0000.0000.0100.0000.0010.0040.0080.0000.007
location_chennai0.0990.0000.0090.0620.0910.0490.0250.0500.0350.0320.0540.0410.0110.0240.0130.0000.0260.1010.1500.0720.0340.1150.0260.0240.1060.0120.0000.0680.0070.0120.0110.0950.0030.0190.0040.0050.0110.0221.0000.0140.0180.0060.0070.0360.0170.0200.0390.0090.0850.0160.0060.0070.0640.0050.0050.0530.0140.0030.0140.0160.0180.0910.0030.0160.0060.0000.0120.0220.0250.0070.0140.0080.0150.0000.0000.0990.0200.0000.0130.0100.0160.0000.0270.0130.0040.0190.0030.0030.0110.0000.0160.0270.0250.0080.0040.0050.0080.0050.0030.0280.0060.0090.0130.0240.0030.022
location_coimbatore0.0390.0000.0000.0210.0210.0270.0000.0050.0050.0020.0080.0080.0000.0150.0020.0000.0160.0080.0080.0200.0060.0160.0040.0030.0000.0000.0000.0160.0000.0000.0000.0220.0000.0030.0000.0000.0000.0040.0141.0000.0030.0000.0000.0080.0020.0030.0090.0000.0190.0020.0000.0000.0140.0000.0000.0120.0010.0000.0010.0020.0030.0210.0000.0020.0000.0000.0000.0040.0050.0000.0010.0000.0010.0000.0000.0230.0040.0000.0000.0000.0020.0000.0050.0000.0000.0030.0000.0000.0000.0000.0020.0050.0050.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0050.0000.004
location_dehradun0.0280.0110.0000.0120.0130.0250.0050.0210.0250.0070.0050.0000.0000.0170.0030.0000.0160.0050.0080.0000.0090.0050.0100.0110.0010.0020.0000.0200.0000.0020.0010.0280.0000.0050.0000.0000.0010.0060.0180.0031.0000.0000.0000.0100.0040.0050.0110.0000.0250.0030.0000.0000.0190.0000.0000.0150.0030.0000.0030.0030.0040.0270.0000.0040.0000.0000.0020.0060.0070.0000.0030.0000.0030.0000.0000.0290.0050.0000.0020.0000.0040.0000.0070.0020.0000.0050.0000.0000.0010.0000.0030.0070.0070.0000.0000.0000.0000.0000.0000.0080.0000.0000.0020.0060.0000.006
location_durgapur0.0420.0000.0000.0160.0180.0050.0000.0100.0090.0020.0000.0030.0000.0000.0000.0000.0000.0060.0040.0030.0000.0100.0270.0200.0030.0000.0000.0070.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0060.0000.0001.0000.0000.0030.0000.0000.0030.0000.0090.0000.0000.0000.0070.0000.0000.0050.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.000
location_ernakulam0.0120.0000.0000.0050.0050.0000.0120.0000.0060.0010.0050.0040.0000.0150.0000.0000.0150.0100.0040.0070.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0070.0000.0000.0001.0000.0030.0000.0000.0040.0000.0100.0000.0000.0000.0070.0000.0000.0060.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.000
location_faridabad0.0550.0000.0090.0720.0400.0160.0270.0530.0360.0150.0280.0230.0030.0400.0080.0000.0380.0280.0680.0310.0120.0090.0040.0000.0210.0070.0000.0400.0030.0070.0060.0570.0000.0110.0000.0020.0060.0130.0360.0080.0100.0030.0031.0000.0100.0110.0230.0050.0500.0090.0030.0030.0380.0020.0020.0310.0080.0000.0080.0090.0110.0540.0000.0090.0030.0000.0060.0130.0140.0030.0080.0040.0090.0000.0000.0590.0120.0000.0070.0050.0090.0000.0160.0070.0000.0110.0000.0000.0060.0000.0090.0160.0150.0040.0000.0020.0040.0020.0000.0170.0030.0050.0070.0140.0000.013
location_ghaziabad0.0260.0000.0000.0270.0130.0350.0070.0230.0290.0020.0000.0000.0000.0240.0030.0000.0230.0050.0120.0260.0000.0070.0000.0000.0050.0010.0000.0190.0000.0010.0010.0270.0000.0040.0000.0000.0010.0050.0170.0020.0040.0000.0000.0101.0000.0050.0110.0000.0240.0030.0000.0000.0180.0000.0000.0150.0030.0000.0020.0030.0040.0260.0000.0040.0000.0000.0010.0060.0060.0000.0030.0000.0030.0000.0000.0290.0050.0000.0020.0000.0030.0000.0070.0020.0000.0040.0000.0000.0010.0000.0030.0070.0070.0000.0000.0000.0000.0000.0000.0080.0000.0000.0020.0060.0000.006
location_goa0.0340.0000.0000.0210.0460.0360.0450.0370.0090.0250.0040.0120.0030.0160.0040.0000.0170.0160.0060.0030.0030.0060.0060.0030.0070.0020.0000.0220.0000.0020.0020.0310.0000.0050.0000.0000.0020.0060.0200.0030.0050.0000.0000.0110.0051.0000.0130.0000.0280.0040.0000.0000.0210.0000.0000.0170.0030.0000.0030.0040.0050.0300.0000.0040.0000.0000.0020.0070.0070.0000.0030.0000.0040.0000.0000.0320.0060.0000.0030.0010.0040.0000.0080.0030.0000.0050.0000.0000.0020.0000.0040.0080.0080.0000.0000.0000.0000.0000.0000.0090.0000.0000.0030.0070.0000.007
location_greater-noida0.0720.0000.0000.1550.0290.1910.0220.0270.0130.0000.3850.5170.0050.0150.0080.0000.0140.0600.0310.1100.0280.0100.0110.0090.0140.0070.0000.0440.0040.0070.0070.0620.0000.0120.0010.0030.0070.0140.0390.0090.0110.0030.0040.0230.0110.0131.0000.0050.0550.0100.0030.0040.0410.0030.0030.0340.0090.0000.0090.0100.0120.0600.0000.0100.0030.0000.0070.0140.0160.0040.0090.0050.0090.0000.0000.0650.0130.0000.0080.0060.0100.0000.0180.0080.0020.0120.0000.0000.0070.0000.0100.0180.0160.0040.0020.0030.0040.0030.0000.0180.0030.0050.0080.0160.0000.014
location_guntur0.0320.0000.0000.0250.0160.0200.0330.0160.0030.0010.0040.0050.0030.0000.0000.0000.0000.0050.0000.0110.0040.0030.0030.0030.0170.0000.0000.0100.0000.0000.0000.0150.0000.0000.0000.0000.0000.0010.0090.0000.0000.0000.0000.0050.0000.0000.0051.0000.0130.0000.0000.0000.0090.0000.0000.0080.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0010.0020.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0030.0020.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0020.0000.001
location_gurgaon0.2980.0050.0130.2630.3270.1600.0740.0600.0140.0300.0590.0030.1130.1050.0210.0000.1010.0390.0430.1300.0350.0220.0320.0330.0210.0170.0030.0950.0100.0170.0160.1330.0050.0260.0070.0080.0160.0300.0850.0190.0250.0090.0100.0500.0240.0280.0550.0131.0000.0220.0090.0100.0890.0080.0080.0740.0200.0050.0200.0220.0260.1280.0050.0230.0090.0020.0170.0310.0350.0100.0200.0120.0210.0030.0030.1390.0290.0030.0180.0140.0220.0030.0380.0180.0070.0260.0050.0050.0160.0030.0220.0380.0350.0110.0070.0080.0110.0080.0050.0400.0090.0130.0190.0340.0050.031
location_guwahati0.0500.0360.0000.0220.0360.0180.0020.0260.0280.0080.0050.0090.0510.0310.0020.0000.0300.0050.0070.0080.0060.0000.0000.0010.0050.0000.0000.0180.0000.0000.0000.0250.0000.0040.0000.0000.0000.0050.0160.0020.0030.0000.0000.0090.0030.0040.0100.0000.0221.0000.0000.0000.0160.0000.0000.0140.0020.0000.0020.0030.0040.0240.0000.0030.0000.0000.0000.0050.0060.0000.0020.0000.0020.0000.0000.0260.0040.0000.0010.0000.0030.0000.0060.0010.0000.0040.0000.0000.0000.0000.0030.0060.0060.0000.0000.0000.0000.0000.0000.0070.0000.0000.0020.0060.0000.005
location_gwalior0.0270.0000.0000.0060.0110.0050.0030.0010.0000.0000.0030.0020.0000.0100.0000.0000.0110.0150.0070.0080.0030.0030.0000.0000.0020.0000.0000.0070.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0030.0000.0000.0030.0000.0090.0001.0000.0000.0070.0000.0000.0050.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.000
location_haridwar0.0590.0000.0000.0080.0330.0110.0050.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0040.0030.0000.0040.0000.0010.0040.0000.0000.0080.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0030.0000.0000.0040.0000.0100.0000.0001.0000.0070.0000.0000.0060.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.000
location_hyderabad0.1880.0000.0000.1300.0920.0690.0130.0600.0700.0290.0540.0430.0130.0490.0130.0000.0470.0220.0390.0960.0330.0360.0300.0270.1310.0120.0010.0710.0070.0120.0120.1000.0030.0190.0050.0060.0120.0230.0640.0140.0190.0070.0070.0380.0180.0210.0410.0090.0890.0160.0070.0071.0000.0060.0060.0560.0150.0030.0150.0170.0190.0960.0030.0170.0070.0000.0120.0230.0260.0080.0150.0090.0160.0010.0000.1040.0210.0010.0130.0110.0170.0000.0290.0130.0050.0200.0030.0030.0120.0010.0160.0290.0260.0080.0050.0060.0080.0060.0030.0300.0070.0090.0140.0250.0030.023
location_indore0.0130.0000.0000.0150.0110.0000.0000.0070.0070.0010.0040.0010.0000.0030.0000.0000.0030.0070.0030.0090.0000.0060.0000.0000.0020.0000.0000.0060.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0020.0000.0000.0030.0000.0080.0000.0000.0000.0061.0000.0000.0040.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_jabalpur0.0290.0000.0000.0030.0130.0110.0000.0020.0030.0010.0000.0000.0000.0120.0000.0000.0120.0130.0060.0080.0000.0020.0000.0000.0000.0000.0000.0060.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0020.0000.0000.0030.0000.0080.0000.0000.0000.0060.0001.0000.0040.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_jaipur0.1800.0000.0000.0530.0640.0800.0070.0740.0700.0250.0150.0060.0150.1600.0130.0020.1570.0020.0250.0560.0250.0290.0190.0180.1070.0100.0000.0590.0060.0100.0090.0830.0020.0160.0030.0040.0100.0190.0530.0120.0150.0050.0060.0310.0150.0170.0340.0080.0740.0140.0050.0060.0560.0040.0041.0000.0120.0020.0120.0140.0160.0800.0020.0140.0050.0000.0100.0190.0220.0060.0120.0070.0130.0000.0000.0870.0180.0000.0110.0090.0140.0000.0240.0110.0040.0160.0020.0020.0090.0000.0140.0240.0220.0070.0040.0040.0060.0040.0020.0250.0050.0080.0120.0210.0020.019
location_jamshedpur0.0530.0000.0000.0230.0420.0240.0000.0170.0180.0060.0100.0020.0110.0060.0020.0000.0070.0170.0050.0120.0040.0000.0010.0020.0050.0000.0000.0160.0000.0000.0000.0230.0000.0030.0000.0000.0000.0040.0140.0010.0030.0000.0000.0080.0030.0030.0090.0000.0200.0020.0000.0000.0150.0000.0000.0121.0000.0000.0010.0020.0030.0220.0000.0020.0000.0000.0000.0040.0050.0000.0010.0000.0020.0000.0000.0240.0040.0000.0000.0000.0020.0000.0060.0000.0000.0030.0000.0000.0000.0000.0020.0060.0050.0000.0000.0000.0000.0000.0000.0060.0000.0000.0010.0050.0000.004
location_jodhpur0.0200.0000.0000.0030.0040.0000.0050.0000.0000.0000.0000.0000.0000.0070.0000.0000.0070.0040.0000.0060.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0030.0000.0000.0020.0001.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_kalyan0.0530.0000.0000.0080.0960.0080.0000.0120.0110.0820.0410.0090.0010.0260.0000.0000.0260.0150.0070.0120.0030.0000.0000.0000.0020.0000.0000.0160.0000.0000.0000.0220.0000.0030.0000.0000.0000.0040.0140.0010.0030.0000.0000.0080.0020.0030.0090.0000.0200.0020.0000.0000.0150.0000.0000.0120.0010.0001.0000.0020.0030.0210.0000.0020.0000.0000.0000.0040.0050.0000.0010.0000.0020.0000.0000.0230.0040.0000.0000.0000.0020.0000.0060.0000.0000.0030.0000.0000.0000.0000.0020.0060.0050.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0050.0000.004
location_kanpur0.0340.0000.0000.0110.0270.0200.0210.0000.0130.0060.0070.0090.0090.0080.0020.0000.0080.0000.0000.0000.0020.0050.0000.0000.0020.0000.0000.0180.0000.0000.0000.0250.0000.0040.0000.0000.0000.0050.0160.0020.0030.0000.0000.0090.0030.0040.0100.0000.0220.0030.0000.0000.0170.0000.0000.0140.0020.0000.0021.0000.0040.0240.0000.0030.0000.0000.0000.0050.0060.0000.0020.0000.0020.0000.0000.0260.0040.0000.0010.0000.0030.0000.0060.0010.0000.0040.0000.0000.0000.0000.0030.0060.0060.0000.0000.0000.0000.0000.0000.0070.0000.0000.0020.0060.0000.005
location_kochi0.0200.0000.0000.0150.0430.0480.0210.0140.0000.0060.0140.0120.0000.0230.0030.0000.0240.0320.0100.0210.0080.0070.0050.0050.0060.0020.0000.0210.0000.0020.0010.0290.0000.0050.0000.0000.0010.0060.0180.0030.0040.0000.0000.0110.0040.0050.0120.0000.0260.0040.0000.0000.0190.0000.0000.0160.0030.0000.0030.0041.0000.0280.0000.0040.0000.0000.0020.0060.0070.0000.0030.0000.0030.0000.0000.0310.0050.0000.0020.0010.0040.0000.0080.0020.0000.0050.0000.0000.0010.0000.0040.0080.0070.0000.0000.0000.0000.0000.0000.0080.0000.0000.0030.0070.0000.006
location_kolkata0.2180.0000.0000.2240.1770.0420.1030.1100.0460.0440.0790.0620.0200.0790.1570.0000.0550.0730.0690.0310.0460.1400.2110.0870.0280.0180.0030.1030.0110.0180.0170.1440.0060.0280.0080.0090.0170.0330.0910.0210.0270.0100.0110.0540.0260.0300.0600.0140.1280.0240.0100.0110.0960.0090.0090.0800.0220.0060.0210.0240.0281.0000.0060.0250.0100.0030.0180.0340.0380.0110.0220.0130.0230.0030.0030.1500.0310.0030.0190.0160.0240.0030.0410.0190.0080.0290.0060.0060.0170.0030.0240.0410.0380.0120.0080.0090.0120.0090.0060.0430.0100.0140.0200.0370.0060.034
location_kozhikode0.0090.0000.0000.0080.0020.0040.0090.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0200.0000.0000.0000.0020.0000.0000.0040.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0030.0000.0000.0020.0000.0000.0000.0000.0000.0061.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_lucknow0.0300.0000.0000.0290.0060.0230.0040.0050.0020.0000.0050.0060.0000.0030.0030.0000.0030.0050.0040.0000.0030.0070.0000.0040.0040.0010.0000.0180.0000.0010.0000.0260.0000.0040.0000.0000.0000.0050.0160.0020.0040.0000.0000.0090.0040.0040.0100.0000.0230.0030.0000.0000.0170.0000.0000.0140.0020.0000.0020.0030.0040.0250.0001.0000.0000.0000.0010.0050.0060.0000.0020.0000.0030.0000.0000.0270.0050.0000.0010.0000.0030.0000.0070.0010.0000.0040.0000.0000.0000.0000.0030.0070.0060.0000.0000.0000.0000.0000.0000.0070.0000.0000.0020.0060.0000.005
location_ludhiana0.0070.0000.0000.0220.0030.0000.0080.0070.0000.0020.0050.0040.0000.0000.0000.0000.0000.0080.0000.0060.0000.0000.0000.0000.0020.0000.0000.0070.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0030.0000.0000.0030.0000.0090.0000.0000.0000.0070.0000.0000.0050.0000.0000.0000.0000.0000.0100.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.000
location_madurai0.0090.0000.0000.0030.0120.0000.0010.0000.0000.0000.0000.0000.0000.0060.0000.0000.0060.0000.0000.0030.0000.0080.0000.0000.0000.0000.0000.0010.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_mangalore0.0290.0000.0000.0130.0160.0180.0000.0180.0180.0050.0010.0070.0000.0090.0000.0000.0100.0090.0020.0090.0000.0050.0000.0030.0060.0000.0000.0130.0000.0000.0000.0190.0000.0020.0000.0000.0000.0030.0120.0000.0020.0000.0000.0060.0010.0020.0070.0000.0170.0000.0000.0000.0120.0000.0000.0100.0000.0000.0000.0000.0020.0180.0000.0010.0000.0001.0000.0030.0040.0000.0000.0000.0000.0000.0000.0200.0030.0000.0000.0000.0010.0000.0040.0000.0000.0020.0000.0000.0000.0000.0000.0040.0040.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0040.0000.003
location_mohali0.0330.0000.0190.0510.0270.0090.0040.0400.0440.0000.0130.0140.0040.0230.0050.0000.0220.0220.0140.0470.0140.0060.0030.0000.0120.0030.0000.0250.0000.0030.0030.0350.0000.0060.0000.0000.0030.0070.0220.0040.0060.0000.0000.0130.0060.0070.0140.0010.0310.0050.0000.0000.0230.0000.0000.0190.0040.0000.0040.0050.0060.0340.0000.0050.0000.0000.0031.0000.0090.0000.0040.0010.0050.0000.0000.0370.0070.0000.0040.0020.0050.0000.0100.0040.0000.0060.0000.0000.0030.0000.0050.0100.0090.0000.0000.0000.0000.0000.0000.0100.0000.0010.0040.0080.0000.008
location_mumbai0.1480.0000.0000.0430.0570.1690.0070.0240.0190.1580.0940.0000.0020.0000.0000.0000.0000.0080.0050.0100.0000.0060.0070.0040.0120.0040.0000.0280.0000.0040.0030.0390.0000.0070.0000.0000.0030.0080.0250.0050.0070.0000.0000.0140.0060.0070.0160.0020.0350.0060.0000.0000.0260.0000.0000.0220.0050.0000.0050.0060.0070.0380.0000.0060.0000.0000.0040.0091.0000.0000.0050.0020.0050.0000.0000.0410.0080.0000.0040.0030.0060.0000.0110.0040.0000.0070.0000.0000.0030.0000.0060.0110.0100.0010.0000.0000.0010.0000.0000.0110.0000.0020.0050.0090.0000.009
location_mysore0.0160.0000.0000.0100.0090.0110.0020.0000.0000.0000.0030.0040.0000.0000.0000.0000.0000.0050.0000.0100.0000.0000.0000.0000.0020.0000.0000.0080.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0030.0000.0000.0040.0000.0100.0000.0000.0000.0080.0000.0000.0060.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.000
location_nagpur0.0420.0000.0000.0160.0180.0270.0000.0110.0110.0060.0000.0020.0000.0050.0020.0000.0040.0150.0040.0180.0080.0050.0050.0050.0050.0000.0000.0160.0000.0000.0000.0230.0000.0030.0000.0000.0000.0040.0140.0010.0030.0000.0000.0080.0030.0030.0090.0000.0200.0020.0000.0000.0150.0000.0000.0120.0010.0000.0010.0020.0030.0220.0000.0020.0000.0000.0000.0040.0050.0001.0000.0000.0020.0000.0000.0240.0040.0000.0000.0000.0020.0000.0060.0000.0000.0030.0000.0000.0000.0000.0020.0060.0050.0000.0000.0000.0000.0000.0000.0060.0000.0000.0010.0050.0000.004
location_nashik0.0330.0000.0000.0110.0210.0110.0000.0200.0190.0110.0000.0050.0000.0000.0000.0000.0000.0120.0070.0110.0020.0000.0000.0000.0080.0000.0000.0100.0000.0000.0000.0140.0000.0000.0000.0000.0000.0010.0080.0000.0000.0000.0000.0040.0000.0000.0050.0000.0120.0000.0000.0000.0090.0000.0000.0070.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0010.0020.0000.0001.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0020.0020.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0020.0000.001
location_navi-mumbai0.0160.0000.0000.0250.0490.0250.0050.0250.0290.0810.0520.0060.0030.0230.0020.0000.0240.0180.0120.0000.0090.0080.0050.0000.0070.0000.0000.0170.0000.0000.0000.0240.0000.0030.0000.0000.0000.0040.0150.0010.0030.0000.0000.0090.0030.0040.0090.0000.0210.0020.0000.0000.0160.0000.0000.0130.0020.0000.0020.0020.0030.0230.0000.0030.0000.0000.0000.0050.0050.0000.0020.0001.0000.0000.0000.0250.0040.0000.0010.0000.0020.0000.0060.0010.0000.0040.0000.0000.0000.0000.0020.0060.0060.0000.0000.0000.0000.0000.0000.0060.0000.0000.0010.0050.0000.005
location_navsari0.0280.0000.0000.0000.0060.0000.0020.0050.0030.0090.0090.0000.0070.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_nellore0.0090.0000.0000.0060.0010.0000.0040.0020.0000.0000.0000.0000.0000.0050.0000.0000.0050.0020.0000.0020.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_new-delhi0.3120.0000.0000.1770.1400.1000.0000.0730.0750.0550.0890.0680.0170.0300.0240.0000.0330.1010.0600.1990.2290.0590.0410.0390.0830.0200.0040.1120.0120.0200.0190.1570.0070.0310.0080.0100.0190.0360.0990.0230.0290.0110.0120.0590.0290.0320.0650.0150.1390.0260.0110.0120.1040.0100.0100.0870.0240.0070.0230.0260.0310.1500.0060.0270.0110.0030.0200.0370.0410.0120.0240.0150.0250.0040.0041.0000.0340.0040.0210.0170.0260.0040.0450.0210.0080.0310.0060.0060.0190.0040.0260.0450.0410.0130.0080.0100.0130.0100.0070.0470.0110.0150.0220.0400.0070.037
location_noida0.0270.0000.0560.1230.0160.0810.0050.0270.0240.0030.2190.2930.0020.0200.0030.0000.0200.0250.0110.0560.0030.0090.0000.0010.0140.0030.0000.0230.0000.0030.0020.0320.0000.0050.0000.0000.0020.0070.0200.0040.0050.0000.0000.0120.0050.0060.0130.0000.0290.0040.0000.0000.0210.0000.0000.0180.0040.0000.0040.0040.0050.0310.0000.0050.0000.0000.0030.0070.0080.0000.0040.0000.0040.0000.0000.0341.0000.0000.0030.0020.0040.0000.0090.0030.0000.0060.0000.0000.0020.0000.0040.0090.0080.0000.0000.0000.0000.0000.0000.0090.0000.0000.0030.0080.0000.007
location_palakkad0.0080.0000.0000.0040.0000.0000.0020.0010.0000.0020.0030.0000.0070.0010.0000.0000.0010.0050.0000.0040.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_palghar0.1220.0000.0000.0120.0920.0240.0070.0170.0220.0640.0320.0080.0000.0190.0010.0000.0190.0200.0090.0150.0000.0000.0000.0020.0030.0000.0000.0140.0000.0000.0000.0200.0000.0020.0000.0000.0000.0030.0130.0000.0020.0000.0000.0070.0020.0030.0080.0000.0180.0010.0000.0000.0130.0000.0000.0110.0000.0000.0000.0010.0020.0190.0000.0010.0000.0000.0000.0040.0040.0000.0000.0000.0010.0000.0000.0210.0030.0001.0000.0000.0010.0000.0050.0000.0000.0020.0000.0000.0000.0000.0010.0050.0040.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0040.0000.004
location_panchkula0.0330.0000.0000.0760.0530.0140.0060.0040.0090.0000.0070.0070.0000.0150.0000.0000.0150.0190.0080.0300.0000.0060.0000.0030.0060.0000.0000.0110.0000.0000.0000.0160.0000.0010.0000.0000.0000.0020.0100.0000.0000.0000.0000.0050.0000.0010.0060.0000.0140.0000.0000.0000.0110.0000.0000.0090.0000.0000.0000.0000.0010.0160.0000.0000.0000.0000.0000.0020.0030.0000.0000.0000.0000.0000.0000.0170.0020.0000.0001.0000.0000.0000.0030.0000.0000.0010.0000.0000.0000.0000.0000.0030.0030.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0030.0000.002
location_patna0.0380.0000.0000.0180.0410.0270.0000.0150.0160.0030.0080.0070.0000.0030.0030.0000.0030.0150.0060.0180.0000.0000.0080.0000.0000.0010.0000.0180.0000.0010.0000.0250.0000.0040.0000.0000.0000.0050.0160.0020.0040.0000.0000.0090.0030.0040.0100.0000.0220.0030.0000.0000.0170.0000.0000.0140.0020.0000.0020.0030.0040.0240.0000.0030.0000.0000.0010.0050.0060.0000.0020.0000.0020.0000.0000.0260.0040.0000.0010.0001.0000.0000.0070.0010.0000.0040.0000.0000.0000.0000.0030.0070.0060.0000.0000.0000.0000.0000.0000.0070.0000.0000.0020.0060.0000.005
location_pondicherry0.0070.0000.0000.0000.0100.0020.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0040.0020.0000.0020.0000.0050.0000.0000.0000.0000.0000.0010.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_pune0.0190.0000.0000.0290.0620.0410.0040.0400.0430.1330.0740.0070.0000.0190.0010.0000.0190.0500.0210.0340.0120.0120.0080.0100.0000.0040.0000.0310.0010.0040.0040.0430.0000.0080.0000.0000.0040.0090.0270.0050.0070.0000.0010.0160.0070.0080.0180.0030.0380.0060.0000.0010.0290.0000.0000.0240.0060.0000.0060.0060.0080.0410.0000.0070.0000.0000.0040.0100.0110.0010.0060.0020.0060.0000.0000.0450.0090.0000.0050.0030.0070.0001.0000.0050.0000.0080.0000.0000.0040.0000.0060.0120.0110.0020.0000.0000.0020.0000.0000.0120.0000.0030.0050.0110.0000.010
location_raipur0.0640.0000.0000.0170.0150.0170.0000.0180.0170.0020.0020.0060.0000.0070.0010.0000.0060.0200.0040.0160.0030.0000.0030.0030.0010.0000.0000.0140.0000.0000.0000.0200.0000.0020.0000.0000.0000.0030.0130.0000.0020.0000.0000.0070.0020.0030.0080.0000.0180.0010.0000.0000.0130.0000.0000.0110.0000.0000.0000.0010.0020.0190.0000.0010.0000.0000.0000.0040.0040.0000.0000.0000.0010.0000.0000.0210.0030.0000.0000.0000.0010.0000.0051.0000.0000.0020.0000.0000.0000.0000.0010.0050.0040.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0040.0000.004
location_rajahmundry0.0170.0000.0000.0060.0110.0110.0040.0080.0050.0000.0030.0020.0000.0080.0000.0000.0080.0030.0000.0050.0010.0000.0000.0000.0040.0000.0000.0050.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0020.0000.0070.0000.0000.0000.0050.0000.0000.0040.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_ranchi0.0630.0000.0000.0390.0490.0310.0000.0160.0150.0050.0110.0090.0030.0040.0040.0000.0040.0030.0100.0080.0070.0040.0130.0000.0070.0020.0000.0210.0000.0020.0020.0300.0000.0050.0000.0000.0020.0060.0190.0030.0050.0000.0000.0110.0040.0050.0120.0000.0260.0040.0000.0000.0200.0000.0000.0160.0030.0000.0030.0040.0050.0290.0000.0040.0000.0000.0020.0060.0070.0000.0030.0000.0040.0000.0000.0310.0060.0000.0020.0010.0040.0000.0080.0020.0001.0000.0000.0000.0020.0000.0040.0080.0070.0000.0000.0000.0000.0000.0000.0080.0000.0000.0030.0070.0000.006
location_satara0.0350.0000.0000.0080.0310.0080.0030.0030.0060.0050.0000.0000.0000.0070.0000.0000.0070.0080.0030.0040.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0030.0000.0000.0020.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_shimla0.0070.0000.0000.0050.0070.0070.0170.0050.0040.0000.0030.0000.0000.0000.0000.0000.0010.0060.0030.0020.0000.0000.0040.0000.0020.0000.0000.0040.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0030.0000.0000.0020.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_siliguri0.0410.0570.0000.0400.0260.0220.0000.0260.0280.0060.0080.0040.0000.0230.0000.0000.0220.0050.0070.0000.0030.0090.0000.0000.0030.0000.0000.0120.0000.0000.0000.0180.0000.0010.0000.0000.0000.0030.0110.0000.0010.0000.0000.0060.0010.0020.0070.0000.0160.0000.0000.0000.0120.0000.0000.0090.0000.0000.0000.0000.0010.0170.0000.0000.0000.0000.0000.0030.0030.0000.0000.0000.0000.0000.0000.0190.0020.0000.0000.0000.0000.0000.0040.0000.0000.0020.0000.0001.0000.0000.0000.0040.0040.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0030.0000.003
location_solapur0.0130.0000.0000.0020.0120.0000.0000.0020.0000.0000.0000.0000.0000.0050.0000.0000.0050.0050.0000.0040.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_sonipat0.0430.0000.0000.0760.0270.0280.0040.0000.0040.0060.0080.0090.0060.0100.0020.0000.0110.0360.0480.0080.0060.0020.0000.0000.0020.0000.0000.0180.0000.0000.0000.0250.0000.0040.0000.0000.0000.0050.0160.0020.0030.0000.0000.0090.0030.0040.0100.0000.0220.0030.0000.0000.0160.0000.0000.0140.0020.0000.0020.0030.0040.0240.0000.0030.0000.0000.0000.0050.0060.0000.0020.0000.0020.0000.0000.0260.0040.0000.0010.0000.0030.0000.0060.0010.0000.0040.0000.0000.0000.0001.0000.0060.0060.0000.0000.0000.0000.0000.0000.0070.0000.0000.0020.0060.0000.005
location_surat0.0470.0000.0000.0430.0220.0620.0350.0590.0380.0520.0210.0160.0030.0030.0060.0000.0020.0360.0250.0240.0090.0080.0000.0310.0090.0040.0000.0310.0010.0040.0040.0430.0000.0080.0000.0000.0040.0090.0270.0050.0070.0000.0010.0160.0070.0080.0180.0030.0380.0060.0000.0010.0290.0000.0000.0240.0060.0000.0060.0060.0080.0410.0000.0070.0000.0000.0040.0100.0110.0010.0060.0020.0060.0000.0000.0450.0090.0000.0050.0030.0070.0000.0120.0050.0000.0080.0000.0000.0040.0000.0061.0000.0110.0020.0000.0000.0020.0000.0000.0120.0000.0030.0050.0110.0000.010
location_thane0.0210.0000.0000.0240.0900.0830.0030.0240.0260.1020.0540.0140.0080.0220.0060.0000.0230.0260.0100.0120.0090.0090.0040.0030.0060.0040.0000.0280.0000.0040.0040.0400.0000.0070.0000.0000.0040.0090.0250.0050.0070.0000.0000.0150.0070.0080.0160.0020.0350.0060.0000.0000.0260.0000.0000.0220.0050.0000.0050.0060.0070.0380.0000.0060.0000.0000.0040.0090.0100.0000.0050.0020.0060.0000.0000.0410.0080.0000.0040.0030.0060.0000.0110.0040.0000.0070.0000.0000.0040.0000.0060.0111.0000.0010.0000.0000.0010.0000.0000.0110.0000.0020.0050.0100.0000.009
location_thrissur0.0160.0260.0000.0110.0100.0000.0080.0000.0020.0020.0040.0050.0020.0140.0000.0000.0140.0070.0020.0070.0000.0010.0000.0020.0020.0000.0000.0090.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0040.0000.0000.0040.0000.0110.0000.0000.0000.0080.0000.0000.0070.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0020.0011.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0010.0000.000
location_tirupati0.0300.0000.0000.0050.0090.0100.0000.0030.0000.0000.0030.0000.0000.0080.0000.0000.0080.0070.0000.0070.0010.0000.0000.0000.0030.0000.0000.0050.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0020.0000.0070.0000.0000.0000.0050.0000.0000.0040.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_trichy0.0220.0000.0000.0110.0110.0110.0100.0050.0000.0010.0040.0030.0000.0120.0000.0000.0120.0000.0040.0080.0000.0050.0020.0000.0000.0000.0000.0060.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0020.0000.0000.0030.0000.0080.0000.0000.0000.0060.0000.0000.0040.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
location_trivandrum0.0230.0000.0000.0080.0090.0110.0100.0030.0010.0000.0020.0040.0000.0080.0000.0000.0080.0130.0060.0090.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0040.0000.0000.0040.0000.0110.0000.0000.0000.0080.0000.0000.0060.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0020.0010.0000.0000.0001.0000.0000.0000.0020.0000.0000.0000.0010.0000.000
location_udaipur0.0260.0000.0000.0000.0090.0000.0100.0000.0050.0050.0020.0000.0000.0100.0000.0000.0100.0060.0000.0060.0020.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0020.0000.0000.0030.0000.0080.0000.0000.0000.0060.0000.0000.0040.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
location_udupi0.0160.0000.0000.0000.0080.0030.0000.0000.0000.0070.0020.0000.0000.0090.0000.0000.0100.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0030.0000.0000.0020.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
location_vadodara0.0990.0000.0000.0170.0170.0370.0450.0270.0000.0270.0040.0150.0000.0320.0060.0000.0330.0400.0200.0320.0110.0080.0050.0030.0060.0050.0000.0320.0020.0050.0040.0450.0000.0080.0000.0000.0040.0100.0280.0060.0080.0010.0020.0170.0080.0090.0180.0030.0400.0070.0010.0020.0300.0000.0000.0250.0060.0000.0060.0070.0080.0430.0000.0070.0010.0000.0050.0100.0110.0020.0060.0030.0060.0000.0000.0470.0090.0000.0050.0040.0070.0000.0120.0050.0000.0080.0000.0000.0040.0000.0070.0120.0110.0020.0000.0000.0020.0000.0001.0000.0010.0030.0060.0110.0000.010
location_vapi0.1040.0000.0000.0120.0320.0070.0050.0070.0040.0140.0050.0020.0000.0130.0000.0000.0130.0050.0000.0050.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0030.0000.0000.0030.0000.0090.0000.0000.0000.0070.0000.0000.0050.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0011.0000.0000.0000.0000.0000.000
location_varanasi0.0310.0000.0000.0040.0220.0100.0170.0100.0000.0000.0030.0040.0000.0000.0000.0000.0000.0160.0040.0090.0000.0040.0030.0030.0030.0000.0000.0100.0000.0000.0000.0150.0000.0000.0000.0000.0000.0010.0090.0000.0000.0000.0000.0050.0000.0000.0050.0000.0130.0000.0000.0000.0090.0000.0000.0080.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0010.0020.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0030.0020.0000.0000.0000.0000.0000.0000.0030.0001.0000.0000.0020.0000.001
location_vijayawada0.0430.0000.0000.0210.0220.0300.0260.0080.0070.0050.0080.0060.0000.0200.0010.0000.0210.0160.0040.0150.0050.0060.0050.0050.0110.0000.0000.0150.0000.0000.0000.0210.0000.0030.0000.0000.0000.0040.0130.0000.0020.0000.0000.0070.0020.0030.0080.0000.0190.0020.0000.0000.0140.0000.0000.0120.0010.0000.0000.0020.0030.0200.0000.0020.0000.0000.0000.0040.0050.0000.0010.0000.0010.0000.0000.0220.0030.0000.0000.0000.0020.0000.0050.0000.0000.0030.0000.0000.0000.0000.0020.0050.0050.0000.0000.0000.0000.0000.0000.0060.0000.0001.0000.0040.0000.004
location_visakhapatnam0.0640.0000.0000.0320.0460.0460.0130.0440.0360.0050.0060.0140.0000.0040.0050.0000.0050.0010.0190.0310.0110.0280.0080.0080.0270.0040.0000.0270.0000.0040.0030.0380.0000.0070.0000.0000.0030.0080.0240.0050.0060.0000.0000.0140.0060.0070.0160.0020.0340.0060.0000.0000.0250.0000.0000.0210.0050.0000.0050.0060.0070.0370.0000.0060.0000.0000.0040.0080.0090.0000.0050.0020.0050.0000.0000.0400.0080.0000.0040.0030.0060.0000.0110.0040.0000.0070.0000.0000.0030.0000.0060.0110.0100.0010.0000.0000.0010.0000.0000.0110.0000.0020.0041.0000.0000.008
location_vrindavan0.0340.0000.0000.0070.0380.0000.0140.0110.0000.0000.0030.0000.0000.0000.0000.0000.0000.0090.0000.0040.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0030.0000.0000.0020.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
location_zirakpur0.0550.0000.0000.0640.0620.0190.0090.0550.0500.0030.0140.0140.0020.0510.0050.0000.0500.0280.0000.0660.0070.0120.0040.0070.0180.0030.0000.0250.0000.0030.0030.0350.0000.0060.0000.0000.0030.0070.0220.0040.0060.0000.0000.0130.0060.0070.0140.0010.0310.0050.0000.0000.0230.0000.0000.0190.0040.0000.0040.0050.0060.0340.0000.0050.0000.0000.0030.0080.0090.0000.0040.0010.0050.0000.0000.0370.0070.0000.0040.0020.0050.0000.0100.0040.0000.0060.0000.0000.0030.0000.0050.0100.0090.0000.0000.0000.0000.0000.0000.0100.0000.0010.0040.0080.0001.000

Missing values

2025-09-15T10:31:10.575889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-15T10:31:11.778721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Amount(in rupees)FloorBathroomBalconyBHKArealocation_agralocation_ahmadnagarlocation_ahmedabadlocation_allahabadlocation_aurangabadlocation_badlapurlocation_bangalorelocation_belgaumlocation_bhiwadilocation_bhiwandilocation_bhopallocation_bhubaneswarlocation_chandigarhlocation_chennailocation_coimbatorelocation_dehradunlocation_durgapurlocation_ernakulamlocation_faridabadlocation_ghaziabadlocation_goalocation_greater-noidalocation_gunturlocation_gurgaonlocation_guwahatilocation_gwaliorlocation_haridwarlocation_hyderabadlocation_indorelocation_jabalpurlocation_jaipurlocation_jamshedpurlocation_jodhpurlocation_kalyanlocation_kanpurlocation_kochilocation_kolkatalocation_kozhikodelocation_lucknowlocation_ludhianalocation_madurailocation_mangalorelocation_mohalilocation_mumbailocation_mysorelocation_nagpurlocation_nashiklocation_navi-mumbailocation_navsarilocation_nellorelocation_new-delhilocation_noidalocation_palakkadlocation_palgharlocation_panchkulalocation_patnalocation_pondicherrylocation_punelocation_raipurlocation_rajahmundrylocation_ranchilocation_sataralocation_shimlalocation_siligurilocation_solapurlocation_sonipatlocation_suratlocation_thanelocation_thrissurlocation_tirupatilocation_trichylocation_trivandrumlocation_udaipurlocation_udupilocation_vadodaralocation_vapilocation_varanasilocation_vijayawadalocation_visakhapatnamlocation_vrindavanlocation_zirakpurTransaction_New PropertyTransaction_OtherTransaction_Rent/LeaseTransaction_ResaleFurnishing_FurnishedFurnishing_Semi-FurnishedFurnishing_Unfurnishedfacing_Eastfacing_Northfacing_North - Eastfacing_North - Westfacing_Southfacing_South - Eastfacing_South -Westfacing_WestOwnership_Co-operative SocietyOwnership_FreeholdOwnership_LeaseholdOwnership_Power Of Attorney
015.250595101.02.01500.00000000000000000000000000000000000000000000000000000000000000000000100000000000000001001100000000100
116.09789332.02.02473.00000000000000000000000000000000000000000000000000000000000000000000100000000000000001010100000000100
216.454568102.02.02779.00000000000000000000000000000000000000000000000000000000000000000000100000000000000001001100000000100
314.73180211.01.01530.00000000000000000000000000000000000000000000000000000000000000000000100000000000000001001100000000100
416.588099202.02.02635.00000000000000000000000000000000000000000000000000000000000000000000100000000000000001001000000011000
515.31958821.01.01422.00000000000000000000000000000000000000000000000000000000000000000000100000000000000001001100000001000
614.31628641.02.01550.00000000000000000000000000000000000000000000000000000000000000000000100000000000000001001100000000100
715.60727001.02.01422.00000000000000000000000000000000000000000000000000000000000000000000100000000000000001100100000000100
815.60727001.02.01422.00000000000000000000000000000000000000000000000000000000000000000000100000000000000001100100000001000
916.58809933.01.03900.00000000000000000000000000000000000000000000000000000000000000000000100000000000000001001100000000100
Amount(in rupees)FloorBathroomBalconyBHKArealocation_agralocation_ahmadnagarlocation_ahmedabadlocation_allahabadlocation_aurangabadlocation_badlapurlocation_bangalorelocation_belgaumlocation_bhiwadilocation_bhiwandilocation_bhopallocation_bhubaneswarlocation_chandigarhlocation_chennailocation_coimbatorelocation_dehradunlocation_durgapurlocation_ernakulamlocation_faridabadlocation_ghaziabadlocation_goalocation_greater-noidalocation_gunturlocation_gurgaonlocation_guwahatilocation_gwaliorlocation_haridwarlocation_hyderabadlocation_indorelocation_jabalpurlocation_jaipurlocation_jamshedpurlocation_jodhpurlocation_kalyanlocation_kanpurlocation_kochilocation_kolkatalocation_kozhikodelocation_lucknowlocation_ludhianalocation_madurailocation_mangalorelocation_mohalilocation_mumbailocation_mysorelocation_nagpurlocation_nashiklocation_navi-mumbailocation_navsarilocation_nellorelocation_new-delhilocation_noidalocation_palakkadlocation_palgharlocation_panchkulalocation_patnalocation_pondicherrylocation_punelocation_raipurlocation_rajahmundrylocation_ranchilocation_sataralocation_shimlalocation_siligurilocation_solapurlocation_sonipatlocation_suratlocation_thanelocation_thrissurlocation_tirupatilocation_trichylocation_trivandrumlocation_udaipurlocation_udupilocation_vadodaralocation_vapilocation_varanasilocation_vijayawadalocation_visakhapatnamlocation_vrindavanlocation_zirakpurTransaction_New PropertyTransaction_OtherTransaction_Rent/LeaseTransaction_ResaleFurnishing_FurnishedFurnishing_Semi-FurnishedFurnishing_Unfurnishedfacing_Eastfacing_Northfacing_North - Eastfacing_North - Westfacing_Southfacing_South - Eastfacing_South -Westfacing_WestOwnership_Co-operative SocietyOwnership_FreeholdOwnership_LeaseholdOwnership_Power Of Attorney
17782716.28361024.04.042100.00000000000000000000000000000000000000000000000000000000000000000000000000000000010001001100000000100
17782815.89495233.03.031200.00000000000000000000000000000000000000000000000000000000000000000000000000000000010001010100000000100
17782916.29204955.03.051705.00000000000000000000000000000000000000000000000000000000000000000000000000000000010001010001000000100
17783015.75569053.02.03895.00000000000000000000000000000000000000000000000000000000000000000000000000000000010001010001000000100
17783115.31736313.02.031050.00000000000000000000000000000000000000000000000000000000000000000000000000000000011000001001000000100
17783215.65606023.03.031323.00000000000000000000000000000000000000000000000000000000000000000000000000000000011000010100000000100
17783315.52025943.02.031323.00000000000000000000000000000000000000000000000000000000000000000000000000000000010001001001000000100
17783415.84365913.02.031250.00000000000000000000000000000000000000000000000000000000000000000000000000000000010001100100000000100
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